Grid-Connected Solar-WECS: A Database Performance Analysis

Abstract

Integrating hybrid grid-connected Solar Photovoltaic (PV) and Wind Energy Conversion Systems (WECS) is critical for mitigating the intermittency of standalone renewable energy sources and improving grid stability. Objective: This paper presents a database-driven framework to analyze the performance, seasonal dynamics, and database storage optimization of a co-located Solar-WECS using 12 months of high-resolution operational and meteorological data. Methodology: Utilizing time-coincident datasets from NSRDB, NREL WIND Toolkit, and BC-HRRR, the framework evaluates key performance indicators (KPIs) like energy generation, capacity factor, and inverter efficiency. The data pipeline benchmarks multiple storage architectures—including HDF5 and optimized relational/NoSQL schemas—for query latency and throughput. Results: Statistical analysis confirms that the complementary nature of solar and wind resources yields higher annual energy output and improved capacity factors compared to standalone PV systems. Furthermore, the established normalized database framework effectively tracks KPI deviations, offering a scalable foundation for predictive maintenance and intelligent energy management.

Introduction

Summary

The text presents a database-driven performance analysis of a grid-connected hybrid Solar Photovoltaic (PV) and Wind Energy Conversion System (WECS). The main motivation is the growing global demand for clean electricity and the need to reduce greenhouse-gas emissions while maintaining reliable power generation.

Background and Problem

Solar and wind energy are among the most widely deployed renewable technologies, but both are intermittent because their output depends on changing sunlight, temperature, and wind speed. Using only solar or wind can cause generation variability, voltage fluctuations, grid instability, and mismatches between generation and electricity demand.

A Hybrid Renewable Energy System (HRES) combining solar PV and wind can reduce these problems because the two resources are often complementary:

• Solar generation is generally strongest during the daytime.
• Wind generation can be stronger during evening, night, or different seasons.

Combining them can improve the overall capacity factor and reduce dependence on large energy-storage systems.

Research Gap

The text argues that simulation tools such as MATLAB/Simulink and HOMER Pro are useful for preliminary system design but may not fully represent real-world losses, degradation, environmental variability, and grid-interaction behavior over long periods.

Therefore, the study proposes using historical and live operational databases to evaluate actual system performance. This allows researchers to examine energy production, efficiency, power quality, controller response, and environmental effects under realistic operating conditions.

Methodology

The study uses high-resolution environmental and operational data.

Environmental data includes:

• Global Horizontal Irradiance (GHI)
• Direct Normal Irradiance (DNI)
• Diffuse Horizontal Irradiance (DHI)
• Wind speed
• Ambient temperature
• Air pressure and density
• Turbine pitch angle

System data includes:

• DC array voltage
• Real and reactive power
• Grid current
• Total Harmonic Distortion (THD)
• Component temperatures

The database uses time-series and structured storage formats such as HDF5, relational databases, and NoSQL systems. Data preprocessing includes outlier removal, missing-value handling, and timestamp synchronization.

Mathematical Modeling

For the solar PV system, the study evaluates:

• Performance Ratio (PR) – compares actual energy production with the expected theoretical output.
• Solar energy efficiency – assesses the effectiveness of solar energy conversion under different environmental conditions.

For the WECS, the study uses:

• Weibull distribution to characterize wind-speed behavior.
• Wind turbine power curves to estimate electrical output from available wind energy.

MPPT and Control Optimization

Several Maximum Power Point Tracking (MPPT) approaches are compared:

• Perturb and Observe (P&O) – simple but produces steady-state oscillations.
• Fuzzy Logic Control (FLC) – reduces oscillations and improves control behavior.
• FLC combined with PSO or GA – uses optimization algorithms to improve controller performance.

The system also evaluates inverter and grid-control strategies, including vector control for DFIG/PMSG-based wind systems, while monitoring current THD as a measure of grid power quality.

Major Results

The reported results show improvements from advanced control strategies:

• Conventional P&O had a settling time of approximately 0.25 seconds and exhibited significant oscillations.
• FLC reduced steady-state ripple but responded more slowly to sudden environmental changes.
• PSO-optimized FLC achieved convergence in approximately 0.05 seconds, demonstrating faster tracking under rapidly changing conditions.
• A conventional PI controller produced approximately 6.8% current THD, exceeding the stated 5% limit.
• The High-Order Prescribed Convergence Law (HO-PCL) reduced THD to approximately 0.41%.
• This represents about a 94.01% reduction in stator-current THD compared with the PI baseline.

Complementary Solar-Wind Operation

One of the important findings is the complementary behavior of solar and wind generation. Solar PV contributes strongly around midday, while wind generation can compensate during periods of lower solar output, particularly during evening and nighttime periods. This complementary operation can reduce generation gaps and improve the stability of grid-connected renewable generation.

Conclusion

The integration of hybrid grid-connected Solar Photovoltaic (PV) and Wind Energy Conversion Systems (WECS) represents a critical milestone in mitigating the inherent intermittency of standalone renewable energy sources. This paper successfully demonstrated a database-driven performance analysis of a co-located Solar-WECS utilizing twelve months of normalized operational data. By systematically evaluation metrics such as energy generation, inverter efficiency, capacity factor, performance ratio, grid export, and system availability, the study provides a robust framework for assessing hybrid system dynamics under diverse seasonal conditions. The key findings and contributions of this research are Enhanced Energy Yield and Utilization, The statistical analysis confirms that the complementary nature of solar and wind resources yields a higher annual energy output and an improved capacity factor compared to standalone PV configurations. This synergy optimizes grid-export capabilities and maximizes asset utilization throughout the day and across varying seasons. Operational Insights via Performance Metrics,Evaluating performance ratios and inverter efficiencies across a unified database revealed critical trends in system behavior, pinpointing operational bottlenecks and identifying periods of peak efficiency. Data-Driven Asset Management,Beyond historical evaluation, the established normalized database framework serves as a scalable foundation for predictive maintenance strategies and intelligent energy management. By tracking deviations in key performance indicators (KPIs), operators can preemptively identify equipment degradation and optimize dispatch schedules. Ultimately, this study underscores the viability of database-driven analytics in enhancing the reliability and stability of modern power grids. The insights gained from this performance analysis offer valuable benchmarks for system operators and project developers aiming to deploy efficient, large-scale hybrid renewable energy systems.

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Copyright

Copyright © 2026 MD Mister, Dr. Amol Barve, Dr. Nand Kishore. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.