Syed Zulqadar Hassan

Photovoltaic fault detection and maximum power point tracking are commonly treated as separate functions, although both depend on irradiance, temperature, electrical state and data quality. This study develops a physics-guided framework that links measured fault detection to diagnosis-dependent supervisory MPPT while keeping measured diagnostic evidence separate from control-software evidence. The Lahore dataset contains 217,196 timestamped records and 19,452 unique physical fault-event groups from one grid-tied inverter with two monitored MPPT channels. Fault events are separated chronologically at the physical-event level, and calibration and threshold selection use Training + Validation data only. The HGB detector using the complete operational representation achieved 99.46% Test accuracy and macro-F1, with MCC 0.9892 and AUROC 0.9998. Permutation importance showed strong dependence on sequence-availability descriptors; a residual-free and quality-free design using only logged electrical, environmental and grid channels achieved 88.79% Test accuracy. In a separate static P–V software benchmark, the diagnosis-dependent supervisory P&O controller achieved 99.96% mean tracking efficiency and 0.193 V mean simulated voltage-reference oscillation on a restricted 240-event subset. The control experiment contains no converter dynamics or physical time base and the measured diagnosis evidence is limited to one PV installation. External plant validation and converter-level testing are therefore required before broader operational transfer.

Reliable photovoltaic fault diagnosis depends on classifiers that maintain high accuracy under varying environmental conditions while remaining suitable for lightweight implementation. This study presents a one-dimensional convolutional neural network (1D-CNN) for six-class photovoltaic fault detection using normalized electrical features and compares its performance with alternative methods including a linear multiclass support vector machine, inverse-distance k-nearest neighbors, a compact feedforward neural network and a wavelet-assisted decision tree. A photovoltaic array test system was simulated using 39 months of irradiance and temperature data comprising 105,213 records. Six operating states, namely normal operation, small line-to-line fault, large line-to-line fault, open-circuit fault, partial shading and bypass-diode anomaly, were generated using 46,334 operating points obtained after daylight filtering with G ≥ 80 W/m². To prevent data leakage, samples derived from the same irradiance–temperature condition were assigned exclusively to a single subset among training, validation, or testing. The proposed 1D-CNN achieved an accuracy of 98.95% and a macro-F1 score of 98.95% on the held-out test set, with a serialized model size of 0.162 MB. These results indicate that compact one-dimensional convolution can effectively support the proposed weather-driven photovoltaic fault test system. However, validation using measured field-fault data remains necessary before establishing long-term deployment viability.

The integration of Variable Renewable Energy (VRE) sources in power systems is increased for a sustainable environment. However, due to the intermittent nature of VRE sources, formulating efficient economic dispatching strategies becomes challenging. This systematic review aims to elucidate the economic value creation of Artificial Intelligence (AI) in supporting the integration of VRE sources into power systems by reviewing the role of AI in mitigating costs related to balancing, profile, and grid with a focus on its applications for generation and demand forecasting, market design, demand response, storage solutions, power quality enhancement, and predictive maintenance. The proposed study evaluates the AI potential in economic efficiency and operational reliability improvement by analyzing the use cases with various Renewable Energy Resources (RERs), including wind, solar, geothermal, hydro, ocean, bioenergy, hydrogen, and hybrid systems. Furthermore, the study also highlights the development and limitations of AI-driven approaches in renewable energy sector. The findings of this review aim to highlight AI’s critical role in optimizing VRE integration, ultimately informing policymakers, researchers, and industry stakeholders about the potential of AI for an economically sustainable and resilient energy infrastructure.

This systematic review aims to elucidate the economic value creation of Artificial Intelligence (AI) in supporting the integration of Variable Renewable Energy (VRE) sources into power systems. Addressing the economic dispatch challenges associated with integrated power system has become paramount due to the increasing penetration of VRE. This paper reviews the role of AI in mitigating costs related to balancing, profile, and grid with a focus on its applications for generation and demand forecasting, market design, demand response, storage solutions, power quality enhancement, and predictive maintenance. By analyzing the use cases across various renewable energy resources (RERs), including wind, solar, geothermal, hydro, ocean, bioenergy, hydrogen, and hybrid systems, this study highlights AI’s potential to enhance economic efficiency and operational reliability. This review spans the literature from 2014 to 2024, offering insights into the advancements and limitations of AI-driven approaches in the renewable energy sector. The findings underscore AI’s critical role in optimizing VRE integration, ultimately facilitating a more resilient and economically sustainable energy landscape.

Open Access Article Improving Distribution Power System Efficiency using Power Loss Reduction by Installing the Distributed Generations by Usman Asghar 1 , Syed Zulqadar Hassan 1 , Muhammad Ahmed 2 , Arsalan Masood1 , Muhammad Naqi Raza , Tariq Kamal 1 Department of Electrical Engineering, University of Sialkot, 51310 Sialkot, Pakistan 2 Interdisciplinary Research Center … Continue reading "Improving Distribution Power System Efficiency using Power Loss Reduction by Installing the Distributed Generations"

The increasing environmental damage caused by adversarial factors, a growing need for energy, the continued reliance on fossil fuels, which comes with rising costs, and the global push for net-zero emissions targets have drawn significant focus on the global promotion of renewable energy sources

In this paper, energy management and control of a microgrid is developed through supervisor and adaptive neuro-fuzzy wavelet-based control controllers considering real weather patterns and load variations. The supervisory control is applied to the entire microgrid using lower–top level arrangements. The top-level generates the control signals considering the weather data patterns and load conditions, while the lower level controls the energy sources and power converters. The adaptive neuro-fuzzy wavelet-based controller is applied to the inverter. The new proposed wavelet-based controller improves the operation of the proposed microgrid as a result of the excellent localized characteristics of the wavelets. Simulations and comparison with other existing intelligent controllers, such as neuro-fuzzy controllers and fuzzy logic controllers, and classical PID controllers are used to present the improvements of the microgrid in terms of the power transfer, inverter output efficiency, load voltage frequency, and dynamic response.

Two technology opportunities, integration of renewable energy sources and the electrification of vehicles are being encouraged to reduce dependency on fossil fuels and pollution problems. Nevertheless, the huge increase of plug-in hybrid electric vehicles (PHEVs) on roads will cause an additional load in demand, especially at rush hours, and therefore, threatens the stability of existing power grids. Considering PHEV stay for several hours in the workplace, (i.e., university), this may provide an inimitable framework to charge PHEV from wind in the workplace. This paper introduces the possibility of introducing intelligent control of wind power and battery storage units as supplementary power sources for future PHEV charging demands during rush hours. The operation of the proposed algorithm is based on the priority levels of PHEVs charging, and fluctuations in DC link voltage levels due to the variation in wind speed. The priorities of PHEVs charging are developed according to their power requirements, maximum rating of distribution transformer and park duration of PHEVs in the workplace during wind speed. Various non-isolated proportional–integral controllers and improved intelligent fuzzy control are used to keep a minimum critical DC link voltage to permit the power conditioning system to operate a charging station uninterruptedly, even at low wind speed. The improved intelligent fuzzy controller also contributes to minimizing the stress on the DC bus and ensures quality output power. The performance of the proposed charging station is verified for the real PHEV under real-world record of wind speed. All the energy sources, electric charging station and their controllers are designed in MATLAB/Simulink. Finally, the feasibility of proposed charging station is checked experimentally in the laboratory.