Photovoltaic Units Research Articles

THE PERFORMANCE AND ADVANCED COOLING TECHNIQUES FOR PHOTOVOLTAIC SOLAR PANELS

The high performance of the photovoltaic cell requires proper and efficient cooling because the electrical efficiency of the photovoltaic cell is affected by the operating temperature. Providing a suitable operating environment for the photovoltaic cell at a low temperature is necessary, which can be achieved using devices with highly effective thermal performance in which heat is transferred from the cell to the external surroundings via pulsating heat pipes, and this process leads to significant energy generation and the best efficiency of the photovoltaic unit. In this work, we use pulsating heat pipes with copper fins to achieve adequate cooling of the cell and make it operate at the best temperature, leading to a higher photovoltaic cell efficiency. The metal used for the pulsating heat pipes is copper, which has high conductivity needed to increase performance. The study showed that pulsating heat pipes are the most suitable options for cooling the photovoltaic cell, which leads to increasing its efficiency and increasing electricity production.

MIWO based MPPT of PV system for induction motor driven water pumping system

Renewable energy source-based Water Pumping Systems (WPSs) have gained popularity for drinking, agricultural, and industrial purposes. Locally situated standalone Photovoltaic (PV) powered WPS can provide a viable water supply solution, especially with induction motors (IM) due to their advantages over other motor types. However, solar PV energy output is greatly influenced by weather conditions, necessitating energy storage systems like batteries, which increase costs and require maintenance. This research proposes an efficient energy management system for a PV-powered IM-driven WPS that does not rely on batteries. The system handles Partial Shading Conditions (PSC) effectively, thanks to a sensorless speed controller combining a Modified Invasive Weed Optimization (MIWO) mechanism with the Perturb and Observe (P&O) method. This controller uses the inverter as an MPPT circuit, eliminating the need for a distinct converter to follow the MPP. The hybrid P&O-MIWO method's outcomes are compared with those of P&O hybridization with GA, PSO, and GWO to assess efficiency under various PSCs. TS-Fuzzy controllers are employed for voltage control at the DC-link and motor speed. The proposed system is analyzed across different scenarios and validated using a Simulink model in MATLAB. Results indicate that the hybrid P&O-MIWO mechanism enhances energy generation from the PV unit without relying on energy storage devices, providing a cost-effective solution for PV-based WPS implementation.

Enhanced composite controller for PV/PMSG/PEMFC and BESS‐based DC microgrids voltage regulation: Integrating integral terminal sliding mode controller and recursive backstepping controller

AbstractThe variability of renewable energy sources due to weather patterns often leads to a mismatch between power generation and consumption within microgrids (MGs). This challenge is exacerbated when integrating bio‐renewable units, complicating stability maintenance in MGs. This research work introduces a novel solution to address this issue: a composite controller merging an integral terminal sliding mode controller with a recursive backstepping controller for direct current MGs (DCMGs). The proposed DCMG incorporates solar photovoltaic units, wind farms based on permanent magnet synchronous generators, proton exchange membrane fuel cells fuelled by hydrogen gas, an electrolyser, battery energy storage systems, and DC loads. First, a comprehensive mathematical model for the components within the DCMG is developed to design the proposed composite controller. This controller not only overcomes the inherent limitations and convergence issues of conventional SMCs but also ensures stable DC‐bus voltage and maintains power balance across various operational conditions. Moreover, a fuzzy logic‐based energy management system is introduced to regulate power flow, considering factors like battery state of charge and renewable energy sources' total power output. The control Lyapunov function confirms the DCMG system's asymptotic stability. Finally, the proposed controller's effectiveness is validated through simulations on both MATLAB/Simulink and Arduino Mega 2650 processor‐in‐the‐loop platforms under various operational conditions. In both platforms, the proposed controller surpasses an existing controller in terms of settling time, overshoot, and tracking error of the DC‐bus voltage.

Dynamic Wireless Charging of Electric Vehicles Using PV Units in Highways

Transitioning from petrol or gas vehicles to electric vehicles (EVs) poses significant challenges in reducing emissions, lowering operational costs, and improving energy storage. Wireless charging EVs offer promising solutions to wired charging limitations such as restricted travel range and lengthy charging times. This paper presents a comprehensive approach to address the challenges of wireless power transfer (WPT) for EVs by optimizing coupling frequency and coil design to enhance efficiency while minimizing electromagnetic interference (EMI) and heat generation. A novel coil design and adaptive hardware are proposed to improve power transfer efficiency (PTE) by defining the optimal magnetic resonant coupling WPT and mitigating coil misalignment, which is considered a significant barrier to the widespread adoption of WPT for EVs. A new methodology for designing and arranging roadside lanes and facilities for dynamic wireless charging (DWC) of EVs is introduced. This includes the optimization of transmitter coils (TCs), receiving coils (RCs), compensation circuits, and high-frequency inverters/converters using the partial differential equation toolbox (pdetool). The integration of wireless charging systems with smart grid technology is explored to enhance energy distribution and reduce peak load issues. The paper proposes a DWC system with multiple segmented transmitters integrated with adaptive renewable photovoltaic (PV) units and a battery system using the utility main grid as a backup. The design process includes the determination of the required PV array capacity, station battery sizing, and inverters/converters to ensure maximum power point tracking (MPPT). To validate the proposed system, it was tested in two scenarios: charging a single EV at different speeds and simultaneously charging two EVs over a 1 km stretch with a 50 kW system, achieving a total range of 500 km. Experimental validation was performed through real-time simulation and hardware tests using an OPAL-RT platform, demonstrating a power transfer efficiency of 90.7%, thus confirming the scalability and feasibility of the system for future EV infrastructure.

Photo-voltaic system with battery employing parallel converters operation for charging stations and distributed loads amidst anomalous grid

In this work, solar photovoltaic array-based generation unit is connected to non-ideal distribution network and powering plug-in electric vehicle (PEV) charging infrastructure and other local loads connected to AC common bus while ensuring reliable operation of parallel connected voltage source converters under dynamics. A photovoltaic array connected via voltage source inverter, which feeds power to loads and battery is used to act as power management entity and provides reactive power and harmonic power support via converter. In context of widespread incorporation of PEVs and renewable energy sources (RES) into grid, inherent intermittency and dynamic behavior pose significant challenges in establishing a resilient and flexible control framework. Consequently, a solution is proposed that employs dual low pass filter integrated along an adaptive noise elimination filtering algorithm, serving as a primary processing stage. Additionally, integrator-based frequency locked loop control strategy is implemented in conjunction to mitigate power quality concerns, regulate voltage levels, and correct power factor discrepancies. Furthermore, system ensures continuous operation, providing uninterrupted power to local loads even during fluctuating conditions while simultaneously functioning as a seamless PEV charging station. Additionally, a comparative analysis of controller performance is summarized in a tabular format, alongside a power quality assessment. The analysis includes the evaluation of total harmonic distortion (THD) for both grid currents and grid voltages, which are measured at 2.36 % and 3.60 %, respectively. These values are well within the limits established by IEEE 519 standard, demonstrating compliance with the required power quality criteria. This RES based charging structure topology is tested using a hardware prototype under dynamics and impactful performance is observed through obtained results.

Explainable AI and optimized solar power generation forecasting model based on environmental conditions.

This paper proposes a model called X-LSTM-EO, which integrates explainable artificial intelligence (XAI), long short-term memory (LSTM), and equilibrium optimizer (EO) to reliably forecast solar power generation. The LSTM component forecasts power generation rates based on environmental conditions, while the EO component optimizes the LSTM model's hyper-parameters through training. The XAI-based Local Interpretable and Model-independent Explanation (LIME) is adapted to identify the critical factors that influence the accuracy of the power generation forecasts model in smart solar systems. The effectiveness of the proposed X-LSTM-EO model is evaluated through the use of five metrics; R-squared (R2), root mean square error (RMSE), coefficient of variation (COV), mean absolute error (MAE), and efficiency coefficient (EC). The proposed model gains values 0.99, 0.46, 0.35, 0.229, and 0.95, for R2, RMSE, COV, MAE, and EC respectively. The results of this paper improve the performance of the original model's conventional LSTM, where the improvement rate is; 148%, 21%, 27%, 20%, 134% for R2, RMSE, COV, MAE, and EC respectively. The performance of LSTM is compared with other machine learning algorithm such as Decision tree (DT), Linear regression (LR) and Gradient Boosting. It was shown that the LSTM model worked better than DT and LR when the results were compared. Additionally, the PSO optimizer was employed instead of the EO optimizer to validate the outcomes, which further demonstrated the efficacy of the EO optimizer. The experimental results and simulations demonstrate that the proposed model can accurately estimate PV power generation in response to abrupt changes in power generation patterns. Moreover, the proposed model might assist in optimizing the operations of photovoltaic power units. The proposed model is implemented utilizing TensorFlow and Keras within the Google Collab environment.

Multi-strategy improved runge kutta optimizer and its promise to estimate the model parameters of solar photovoltaic modules

Harnessing the potential of solar photovoltaic (PV) technology relies heavily on accurately estimating the model parameters of PV cells/modules using real current-voltage (I-V) data. Achieving optimal parameter values is essential for the performance and efficiency of PV systems, necessitating the use of advanced optimization techniques. In our endeavor, we introduce a multi-strategy improvement approach for the Runge Kutta (RUN) optimizer, a cutting-edge tool used for tackling this critical task in both single-diode and double-diode PV unit models. By aligning experimental and model-based estimated data, our approach seeks to reduce errors and improve the accuracy of PV system performance. We conduct meticulous analyses of two compelling case studies and the CEC 2020 test suite to showcase the versatility and effectiveness of our improved RUN (IRUN) algorithm. The first case study involves a standard dataset derived from the well-known R.T.C. France silicon solar cell, where IRUN performs favorably compared to competing methods, demonstrating its effectiveness. IRUN effectively manages the complex task of defining model parameters for an industrial PV module situated at the Engineering Faculty of Düzce University in Turkey. The real-world I-V data, obtained under optimal conditions with a temperature of 30°C and solar radiance of 1000W/m2, provide strong evidence of the practical applicability and real-world benefits of our innovative method. Additional analyses through three-diode and PV module models further confirm the efficacy of the IRUN. A mean absolute error of down to 6.5E-04 and root mean square error of down to 7.3668E-04 are achieved. Our approach provides a practical and efficient tool for improving the accuracy of PV systems, enhancing their performance when compared to existing methods.

Analysis of the efficiency of a Solar Photovoltaic Power Plant linked to the electrical grid

The growing need for energy in emerging nations gives rise to apprehensions over energy security, so underscoring the criticality of harnessing the potential of renewable resources. Empirical evidence has demonstrated that photovoltaic systems integrated into the network are the optimal options for large-scale renewable energy. The analysis of the performance of these network-connected centrals can enhance the design, operation, and maintenance of new systems. The commissioning of a 12 MW photovoltaic solar power plant in Sidi Bel Abbès, located in the Dhaya region, has established it as one of the largest solar power plants in Algeria. The site yields a mean solar radiation of 5.21 kilowatt-hours per square metre per day and an average yearly temperature of 18.9 degrees Celsius. The present work provides an analysis of the yearly performance and conceptual aspects of the photovoltaic central unit. We compare the performance outcomes of the power plant with the simulated values generated by the SolarGis PvPlanner program. Analysis of real-world performance in relation to simulations allows for the detection of inconsistencies and the enhancement of future initiatives. SolarGis PvPlanner is an essential modelling program for accurately forecasting performance and effectively strategising solar projects, therefore guaranteeing sustainable and dependable energy for the future.

Voltage regulation and energy loss minimization for distribution networks with high photovoltaic penetration and EV charging stations using dual-stage model predictive control

The widespread integration of photovoltaic (PV) units and controllable loads like electric vehicles (EVs) might cause uncertain voltage fluctuations quite frequently. The reactive power from distributed generation (DG) units and EV charging stations (EVCSs) can effectively be used along with conventional devices like on-load tap changer (OLTC) to successfully control network voltages in real-time, with multi-time scale coordination. However, the control of a large number of available resources, with reliable communication among them becomes a complex task. Moreover, the substantial real-time data set amassed through the deployment of measurement devices across the network increases both cost and computational intricacies. This paper presents a novel approach, employing a time decomposition-based dual-stage model predictive control (MPC) with a reduced model control framework for voltage control and energy loss minimization in active distribution networks (ADNs), by significantly reducing the number of measuring devices. A minimum global set of available control resources is identified for real-time control, aiming to mitigate control complexity and minimize the demand for heavy communication. The proposed control strategy is validated on the 33-bus distribution network and modified IEEE 123-bus distributed network, under high PV penetration and EV charging stations. It is seen that the proposed reduced model framework with very limited measuring devices and control equipment can effectively regulate the voltages with a standard deviation of 0.0059 p.u. and 0.0035 p.u. as compared to the full order system model, for 33-bus network and IEEE 123-bus network, respectively. Furthermore, there is a net 23.24% reduction in energy losses when power loss minimization is considered along with the minimization of voltage deviations in the 33-bus network.

Efficiency enhancement of photovoltaic-thermoelectric generator hybrid module by heat dissipating technique

Exploring substantial solar irradiation and recuperating excess heat generated during photovoltaic energy conversion is a critical issue. The efficiency of photovoltaic systems (PV) is significantly depend on the increased operating temperatures encountered by solar radiation. One conceivable option for improving the conversion of solar energy is to integrate a photovoltaic (PV) panel with a thermal-electric generator (TEG) material module to create a hybrid system. This study proposed a parallel PV-TEG hybrid module that effectively harvests the maximum solar energy spectrum while maximizing the use of heat generated by the thermoelectric material to improve the overall system efficiency. The proposed module consists of a photovoltaic unit, thermoelectric material module and passive cooling of fluid channels. The aim of this work was to develop a PV-TEG hybrid system and create an energy simulation model in a MATLAB environment to analyze the model's performance under various operational conditions by applying both theoretical and experimental approaches. Findings showed considerable concurrence. At 13:00, when the PV surface temperature was 54 °C, the PV efficiency reached to its lowest value of 12.0 %. Nevertheless, the highest TEG efficiency recorded was 4.7 % at 12:00 h. The efficiency of the TEG module was significantly affected by weather conditions, inlet cooling water temperature, and fluid flow rate in comparison to both the PV efficiency and the thermal efficiency.

Coordinated planning of thermal power, wind power, and photovoltaic generator units considering capacity electricity price

AbstractWith the implementation of China's carbon reduction policies, the role of thermal power units will transition to a regulating power source. Hence, the electricity market fails to accurately reflect the capacity value of thermal power units, resulting in potential future losses for these units. Therefore, it is imperative to establish a rational capacity compensation mechanism that guarantees the revenue of thermal power units and offers effective investment and construction signals. Therefore, this paper proposes a capacity compensation mechanism for thermal power units based on effective capacity. To achieve this, a two‐layer power source planning model is established. At the upper level of the model, the installed capacity and capacity price of various types of power sources are optimized, while minimizing the operating costs of the planning horizon year under constraints such as annual net profit of units. The lower level focuses on the operation of typical days, optimizing the output of various types of units. Through case analysis, it can be concluded that the proposed model can achieve coordinated planning of capacity and capacity prices for various types of units, effectively ensuring the economic efficiency of the system while safeguarding the revenue of each unit.

Efficient allocation of energy storage and renewable energy system for performance and reliability improvement of distribution system

This paper aims to investigate the impact of optimal planning of renewable energy systems and independent battery energy storage systems (BESS) on the performance and reliability of distribution systems (DS). A unique energy management scheme (EMS) controls the charging and discharging schedules of the BESS. The transit search optimization (TSO) algorithm determines the optimal allocation of wind turbine (WT) and solar photovoltaic (SPV) generator units, along with BESS units. The optimization focuses on improving the DS reliability in terms of energy supply, increasing the feeder security margin, and reducing apparent power loss. Multiple case studies are designed to study the efficacy of the problem outcomes and are validated using Bus 4 of the Roy Bollington Test System (RBTS). To obtain results that are close to actual operating conditions, the modelling process takes into account the time-varying load profile and the dynamic power outputs from the SPV and WT units. The findings show that the suggested method works well by enhancing the feeder security margin by a maximum of 11.25%, reducing expected energy not supplied (EENS) by 4.3%, and improving the power loss profile by 11.5%. In addition, the feeder upgrade deferral (FUD) years also show a maximum improvement of 20.8%.

Design of real-time & historical database for photovoltaic power plant

Abstract The real-time & historical database of photovoltaic power plant monitoring system is used to store and manage the real-time and historical data, including power plant parameters, environmental data, and photovoltaic unit data and so on. Analyzing of this data is helpful for monitoring the status of the plant equipment, performing maintenance, and enhancing the operational efficiency of the power plant. The data models and database function of the database is designed. The data models include tags, data processing, and data storage models. Database function include the modules of the collection, storage, compression, and index query. The data collection module utilizes dynamic link libraries to access database data and configures parameters through a comprehensive data collection framework. The storage strategies involve in a combination of in-memory databases and disk-based historical databases, incorporating secondary caching technology and cloud server storage techniques. The data compression module is achieved through secondary compression methods. The index query module encompass time index files and the secondary index files to improve query speed and real-time system performance. The designed elements collectively establish an efficient monitoring system database.

A two‐stage reactive power optimization method for distribution networks based on a hybrid model and data‐driven approach

AbstractThe uncertainty of distributed energy resources (DERs) and loads in distribution networks poses challenges for reactive power optimization and control timeliness. The computational limitations of the traditional algorithms and the development of artificial intelligence (AI) based technologies have promoted the advancement of hybrid model‐data‐driven algorithms. This article proposes a two‐stage reactive power optimization method for distributed networks (DNs) based on a hybrid model‐data‐driven approach. In the first stage, based on the topology and line parameters of the DN, as well as forecasts of loads and renewable energy outputs, a mixed‐integer second‐order cone programming (MISOCP) algorithm is used to control the on‐load tap changer (OLTC) positions on an hourly day‐ahead basis. In the second stage, leveraging deep learning technology, the real‐time reactive power output of photovoltaics (PV) and wind power units is controlled at a 5‐min time scale throughout the day. Specifically, using traditional solvers, the global optimal reactive power output for PV and wind power units is determined first, corresponding to various load and renewable energy output scenarios. Then, neural networks are trained to map node power to the optimal reactive power outputs of renewable energy units, capturing the complex physical relationships. For the second stage, a transformer network framework with a self‐attention mechanism and multi‐head attention for deep learning training is applied to uncover the intrinsic and physical spatial relationships among high‐dimensional features. The proposed method is tested on a modified IEEE 33‐bus system with multiple distributed renewable energy sources. The case study results demonstrate that the proposed hybrid model‐data‐driven algorithm effectively coordinates day‐ahead and real‐time controls of various devices, achieving real‐time model‐free optimization throughout the day. Compared to traditional deep neural networks (DNNs) and convolutional neural networks (CNNs), the transformer network provides superior reactive power optimization results.

Research on Hybrid Logic Dynamic Model and Voltage Predictive Control of Photovoltaic Storage System

This paper investigates microgrid systems characterized by the coexistence of discrete events and continuous events, a typical hybrid system. By selecting the charging and discharging processes of the energy storage unit as logical variables, a mixed logical dynamic (MLD) model for the microgrid in islanded mode is established. Based on this model, model predictive control (MPC) theory is employed to optimize the energy management strategy, aiming to stabilize the DC bus voltage of the photovoltaic (PV) unit and minimize the switching frequency of the energy storage unit’s charging and discharging processes during system operation.

Algorithmic advancements in agrivoltaics: Modeling shading effects of semi-transparent photovoltaics

Radiation is a crucial factor in the field of agrivoltaics in greenhouses. Depending on the type of photovoltaics integrated into greenhouses, the effect on radiation varies through the phenomenon of shading. Shading in greenhouses can be either beneficial or detrimental, making its analysis imperative. In this study, the improvements and modifications made in an algorithm capable of calculating the shading from photovoltaic units installed in greenhouses are presented. A key modification to the algorithm is the calculation of shading from semi-transparent photovoltaic modules, in contrast to its original form, in which photovoltaic modules were considered opaque. The algorithm was validated using radiation data from pyranometers within a greenhouse. The coefficients used were Pearson's r correlation coefficient and the Coefficient of Variation. The correlation coefficients for times without shading effect approached the values for the case without photovoltaics installed on the roof. Simultaneously, based on the coefficients of variation, the uniformity of radiation within the shade was validated, thereby confirming its existence. Finally, the effect of semi-transparent photovoltaic units on Global Horizontal Irradiance and Photosynthetically Active Radiation was studied, with the reduction for the former approaching 52 % and about 60 % for the latter. These changes in radiation can be either beneficial or not, depending on the type of crop and the needs of the greenhouse, such as cooling.

Multi-objective economic operation of smart distribution network with renewable-flexible virtual power plants considering voltage security index

This paper discusses the simultaneous management of active and reactive power of a flexible renewable energy-based virtual power plant placed in a smart distribution system, based on the economic, operational, and voltage security objectives of the distribution system operator. The formulated problem aims to specify the minimum weighted sum of energy cost, energy loss, and voltage security index, considering the optimal power flow model, voltage security formulation, and the operating model of the virtual power plant. The virtual unit includes renewable sources, like wind systems, photovoltaic, and bio-waste units. Flexibility resources include electric vehicle parking lot and price-based demand response. In the mentioned scheme, parameters of load, renewable sources, electric vehicles, and energy prices are uncertain. This paper utilizes the Unscented Transformation method for modeling uncertainties. Fuzzy decision-making is utilized to extract a compromised solution. The suggested approach innovatively considers the simultaneous management of active and reactive power of a virtual unit with electric vehicles and price-based demand response. This is performed to promote economic, operational, and network security objectives. According to numerical results, the approach with optimal power management of renewable virtual units is capable of boosting the economic, operation, and voltage security status of the network by approximately 43%, 47–62%, and 26.9%, respectively, to power flow studies. Only price-based demand response can improve the voltage security, operation, and economic states of the network by about 19.5%, 35–47%, and 44%, respectively, compared to the power flow model.

The impact of different PV system grid integration approaches on voltage profile, grid losses and behaviour of PV system owners

Distribution grids in individual countries inside the European Union have different properties, while the grid operators use different approaches to integrate photovoltaic (PV) systems into the grid, which influences the voltage profile, grid losses and grid’s hosting capability. This work deals with a 0.4 kV distribution gird characteristic for the Slovenian countryside. The grid users are relatively sparsely distributed in the space. The distances between them are relatively long, while the load distribution among individual phases is unbalanced. Three different approaches to the integration of PV into the grid are discussed. The one used before 2012 with the concentrated larger PV units placed close to the transformer busbars, the one introduced with a net-metering scheme where larger PV units are randomly distributed in the grid, and the one where smaller PV units are evenly distributed throughout the grid. The case study discusses the impacts of the three approaches on the proper voltage profile provision, network losses and behaviour of the PV system owners.

On soft measurement modeling for predicting photovoltaic power with uncertainty based on the Takagi–Sugeno model

Abstract Accurate solar power forecast is becoming more essential for safe and reliable power grid operation with the increasing number of grid-connected photovoltaic (PV) power production units. However, PV power exhibits significant output fluctuation due to both external inputs and intrinsic stochasticity in system dynamics. Therefore, an efficient and reliable soft measurement model of PV power with uncertainty is demanded in practice applications. The technique described in this paper captures the impacts produced by the fundamental uncertainty observed in the data, instead of relying on unrealistic assumptions about uncertainty. A soft measurement model using the Takagi-Sugeno (TS) Fuzzy Logic System (FLS) based on several input-output time series of PV plants is presented in this study. Chebyshev's inequality from probability theory and statistics is adopted to create the confidence interval-based response envelopes for these time series at each moment. An envelope-based measure of output uncertainty and a center-valued response forecasting model can be obtained by the proposed identification technique. PV datasets are employed to demonstrate the concept, which indicates the proposed soft measurement may outperform existing methods in terms of prediction accuracy. The average values of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Correlation Coefficient (R) are only 0.0787, 0.1113, and 0.9979. The average values of the prediction interval coverage probability (PICP) and the prediction interval normalized average width (PINAW) are 0.9806 and 0.1051.

Self-powered, multi-angle gas flow sensing based on photovoltaics with porous graphite electrodes

A self-powered, multi-attack angle gas/air flow sensor without azimuth directional constraints has been designed and developed in this study. Two essential components, the porous graphite cathode and the perovskite photovoltaics, work synergistically in the device. The gas flow penetrates the bulky porous cathode, causing the pressure differences between the upper and lower sides of the nano/micro sheets, leading to the micro lift-force (Bernoulli theory) or dynamic pressure, consequently breaking or forming the electrically conductive paths among the nano/micro sheets. Therefore, the altered electrical conductivity of the cathode prompts a change in the output photocurrent of the perovskite photovoltaic unit. The thin film photovoltaic device design eliminates the requirement for external power sources, enabling multi-attack angle sensing without any azimuth directional limitations.It's important to highlight the scarcity of micro airfoil structures in the gas flow sensing domain by utilizing the nano/micro graphite sheets as the electrode and the thin film photoelectrical device as the power source, which challenges the conventional design typically based on macro-mechanical theory. It is expected that this study could provide researchers with a fresh and distinct perspective for designing and advancing the next generation of multi-angle gas-flow sensors.