دانلود مقاله کنترل و نظارت بر موتور DC خورشیدی با شبکه حسگر بی سیم
چکیده
مقدمه
محاسبه سلول فتوولتائیک
روش جبری موتور DC S.E
الگوریتم کنترل MPPT (اختلال و مشاهده)
کنترلر FPID
شبکه عصبی مصنوعی (ANN)
سیستم شبکه بی سیم (WSN)
نتایج شبیه سازی
نتیجه گیری
منابع
Abstract
Introduction
Calculation of the Photovoltaic cell
S.E DC motor algebraic approach
MPPT control algorithm (perturb and observe)
FPID controller
Artificial Neural Network (ANN)
Wireless Network System (WSN)
Simulation results
Conclusion
Reference
چکیده
با توجه به چالش‌های اقتصادی ناشی از افزایش هزینه‌های نفت و نگرانی‌های طبیعی، هند با رشد چشمگیری در منابع انرژی تجدیدپذیر مواجه است. فناوری فتوولتائیک خورشیدی، به عنوان یک منبع انرژی پاک و سبز، نقش مهمی در رفع کمبود برق هر کشور ایفا می کند. قبل از نصب یک سیستم PV در هر مکانی، مدل‌سازی، شبیه‌سازی و تجزیه و تحلیل ژنراتورهای فتوولتائیک خورشیدی (PV) یک مرحله حیاتی است که به درک رفتار و ویژگی‌های سیستم در شرایط آب و هوایی واقعی کمک می‌کند. یک پنل خورشیدی، مبدل باک-بوست، موتور DC با تحریک جداگانه (S.E) و کنترل‌کننده‌هایی مانند کنترل‌کننده‌های مشتق انتگرال متناسب (PID)، فازی، فازی مبتنی بر PID (FPID) و شبکه عصبی مصنوعی (ANN) همگی شامل می‌شوند. در سیستم پیشنهادی زیرا در مقایسه با سایر کنترلرها، کنترلر ANN نتایج بهتری تولید می کند. برای مقابله با غیر خطی بودن سیستم و ابهام مربوط به سیستم طراحی شده است. شبکه حسگر بی سیم (WSN) و داشبورد داده برای LabVIEW برای نظارت و کنترل سیستم استفاده می شود.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
Due to the economic challenges of soaring oil costs and natural concerns, India faces an enormous growth in renewable energy resources. The solar photovoltaic technology, as a clean and green energy source, plays a critical role in addressing any country’s power shortage. Before mounting a PV system at any location, modeling, simulation, and analysis of solar photovoltaic (PV) generators is a critical phase that helps to understand the behavior and characteristics of the system in real-world climatic conditions. A solar panel, buck–boost converter, separately excited (S.E) DC motor, and controllers, such as Proportional Integral Derivative (PID), Fuzzy, fuzzy based on PID (FPID), and Artificial Neural Network (ANN) controllers are all included in the proposed system. Because compared to other controllers, the ANN controller produces better results. It was designed to deal with system non-linearity and system-related ambiguity. Wireless sensor network (WSN) and Data Dashboard for LabVIEW are used to monitor and control the system.
Introduction
With growing concerns about air pollution and global warming, clean renewable energy sources are expected to play an important role or play a major role in the global strength of the future. If we want to ensure that every citizen in the country has access to electricity 24 h a day, seven days a week, the most promising solution is solar power. Geographically, India has ideal properties for solar energy. Solar radiation provides the core of the most promising and lasting light source, and its superior performance can be leveraged by artificial intelligence to achieve the best possible performance. As the acceptance of LEDs, low voltage electronics and efficient DC motor technology increases, household energy needs to be covered using direct current flowing directly from solar panels. This can reduce energy consumption by more than 50%. A photovoltaic system converts solar radiation into direct current. The use of PV structures as a power source for electrical machinery is seen as a promising region for PV packaging as the PV market continues to boom. Dynamic and steady-state characteristics of photovoltaic S.E. DC motors for various solar intensities, special load conditions, and special device control have been proposed. Today, the mechanical technology has reached high peak utilization, efficiency and cost analysis have skyrocketed, and is a major concern in the development of low-power motor drives for industrial and home use.
Conclusion
In this article, it has been illustrated that for variable intensity and variable weather conditions fixed power is delivered to the S.E DC motor. Simulation work is done to monitor and control a 200W solar panel. The hardware for a 5W solar panel has been managed to complete. The control of the S.E DC motor is done separately using different controllers. Compatibility for the proposed system, regardless of the climate circumstance, has been verified using LabVIEW-based simulation results. The proposed system’s modeling, design, and simulation were carried out in a LabVIEW-based GUI environment. In supervised learning, the ANN controller learns from experience and is more responsive than the PID, Fuzzy, and FPID controllers. From Table 2, it has been depicted that by the use of ANN, no overshoot is present in the output, 64.82% improvement in settling time, 2.5% improvement in Damping Ratio, 7.6% improvement in Peak time, 15.78% improvement in Rise time as compared to FPID controller in speed control of DC motor applications. ANN controller has a significant improvement in the transient and steady-state response of the system over FPID, Fuzzy, and PID controller. Data dashboard and NI WSN are a good choice for wireless monitoring of the system.