Professor Farhad Shahnia

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.

Wireless power transfer (WPT) has emerged as a promising solution for electric vehicle (EV) charging due to its operational convenience, safety, and potential for automated operation. While inductive power transfer (IPT) has been widely implemented, it suffers from issues such as magnetic interference, high coil weight, and electromagnetic exposure. Capacitive power transfer (CPT) offers an alternative with advantages such as lower weight, lighter structure, and reduced electromagnetic interference. CPT systems often exhibit high-frequency operation and have plate-based coupling. This paper proposes four horizontally aligned six-plate CPT coupler configurations and compares their coupling performance under identical boundary conditions. The simulated coupling capacitance for the four couplers ranges from approximately 22 pF to 52 pF at a 150 mm air gap, while the circularplate coupler (HCC2) shows only about 10 - 12\% reduction under \pm 150-\text{mm} lateral misalignment. Additionally, all couplers maintain stable operation across 100-250 \text{mm} air-gap variation, validating suitability for EV underbody installation. The comparative analysis includes electric field intensity, coupling capacitance variation with plate length, misalignment tolerance, and air-gap dependence. These results provide design insights for horizontally aligned CPT couplers for EV charging.

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.

Massive integration of distributed generators with inherently high intermittency and volatility leads to frequent voltage violations. Thus, an appropriate voltage/Var control (VVC) is essential for the secure and economic operation of distribution systems. The increasing penetrations of rooftop photovoltaic units strengthens the coupling between the medium and low voltage (MV-LV) distribution networks spatially, causes temporal interference of VVCs with each other on different timescales, and worsens the network unbalance profile especially on the LV sides. To address this challenge, this study proposes a spatial-temporal coordinated VVC for MV-LV unbalanced distribution networks. Based on 'decomposition-coordination' principle, the developed strategy consists of three modules of real-time evaluation of reactive capability of LV feeders, long-short-time coordinated VVC of MV network, and parallel short-time VVC of LV feeders. Based on the proposed strategy, the VVC optimization problems for these modules are formulated and jointly solved by mixed-integer second-order cone programming and linear programming methods, to minimize the voltage deviations within the MV-LV unbalanced distribution systems in real time. Finally, based on the joint simulation platform of MATLAB and Python, the effectiveness and superiority of the proposal are numerically verified on a real Australian distribution system.

The goals of this study are to ensure simultaneously a good control of energy injection into the network and power quality enhancement. Firstly, the injection mode of different levels of active power via the solar inverter is achieved through intelligent artificial controller. Then, to obtain a sinusoidal grid current waveform, it is proposed to compensate unwanted lower current harmonics caused by the nonlinear load connected at the NCP (Network Coupling Point) by active filtering strategy. Note that, the harmonics produced by the VSI (Voltage Source Inverter) converter due to its switching action are suppressed by a low-pass passive damping LCL-filter. The obtained network current is well filtered, it has a sine waveform and therefore its calculated THD is less than 5% that is verified the IEEE Std 5192014, recommended harmonic limits for current distortion in electrical systems.

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.

Uncertainty in renewable energy resources and variations in the demand response (DR) of participation pose significant challenges for accurately predicting participation levels, particularly across seasons. This study offers a holistic approach to seasonal DR scheduling within the AC optimal power flow (AC-OPF) framework, focusing on advanced thermal energy storage (TES) and optimal energy storage system (ESS) allocation. In this study, we categorized the electrical loads and energy generation from solar and wind sources on a seasonal basis. By analyzing historical load data using the K-means clustering method, we identified key scenarios for implementing effective seasonal demand response (DR) strategies. Additionally, we forecast the optimal participation rates for subscribers, enabling the design of targeted DR incentives that meet the seasonal system needs. Our research specifically targets the optimization of heating and cooling demands within HVAC systems, emphasizing the impact of ambient temperature on the efficiency of TES. This relaxed mixed-integer nonlinear programming (RMINLP) problem was solved using GAMS software with the CONOPT3 solver, specifically applied to the IEEE 24 and 118 bus networks. This multi-objective optimization framework enhances energy management and facilitates the integration of renewable resources, thereby contributing to grid stability and sustainability. Our findings highlight the crucial role of seasonal DR strategies for enhancing the overall efficiency of energy systems. For each season, an optimal range and point for DR performance was obtained to be offered to subscribers by the system operator.