دانلود مقاله تاثیر آب و هوای نگهداری و بالا رفتن عمر در تولید برق
خلاصه
معرفی
بررسی ادبیات
پارامترهای تولید برق خورشیدی و پیش پردازش داده ها
روش شناسی
نتایج
نتیجه گیری و کار آینده
منابع
Abstract
Introduction
Literature Review
Solar Power Generation Parameters and Data Pre-Processing
Methodology
Results
Conclusions and Future Work
References
چکیده
پیش بینی انرژی خورشیدی با استفاده از پارامترهای آب و هوا و فتوولتائیک (PV) رشد فوق العاده ای داشته است. این مطالعه رویکرد جدیدی را ارائه می‌کند که تولید انرژی خورشیدی را با استفاده از فعالیت‌های تعمیر و نگهداری برنامه‌ریزی‌نشده و داده‌های آب‌وهوا پیش‌بینی می‌کند. مجموعه داده از نیروگاه خورشیدی 1 مگاواتی PDEU (دانشگاه ما) به دست آمده است که دارای 12 ستون ساختاری و 1 ستون بدون ساختار با ورودی های متنی دستی در مورد فعالیت های مختلف نگهداری برنامه ریزی شده و برنامه ریزی نشده و شرایط آب و هوایی روزانه است. ستون بدون ساختار برای ایجاد ویژگی های جدید با استفاده از Hash-Map، علامت گذاری کلمات و کلمات توقف استفاده می شود. پیش‌بینی تولید برق خورشیدی به‌عنوان یک مسئله بهینه‌سازی رگرسیون خودکار برداری (VAR) فرموله شده است و پیش‌بینی تولید کل انرژی با نتایج چهار مورد مختلف ارائه شده است. نتایج نشان داده است که ریشه میانگین مربع درصد خطا (RMSPE) در پیش‌بینی تولید برق کل برای مقادیر مختلف تاخیر (p) کمتر از 10 درصد است. رگرسیون خودکار برداری می تواند فعالیت های نگهداری برنامه ریزی نشده مانند خرابی شبکه، خرابی اینورتر، فعالیت تعمیر و نگهداری برنامه ریزی شده مانند تمیز کردن ماژول، فعالیت آب و هوا مانند ابری همراه با پیش بینی کل تولید برق را برای مدیریت موثر و کارآمد نیروگاه های خورشیدی پیش بینی کند. پوسیدگی تولید برق برای همه مجموعه‌های PV متفاوت است که تغییرات در تأثیرات آب و هوا، پیری و نگهداری بر روی نیروگاه خورشیدی را نشان می‌دهد. این کار تحقیقاتی ثابت کرده است که پیک‌های پیش‌بینی و پیش‌بینی تولید برق کل را می‌توان با استفاده از فعالیت‌های نگهداری برنامه‌ریزی‌نشده روزانه و شرایط آب و هوایی به روشی بهتر ردیابی کرد.
Abstract
Solar energy forecasting has seen tremendous growth by using weather and photovoltaic (PV) parameters. This study presents new approach that predicts solar energy production by using the scheduled, unscheduled maintenance activities and weather data. The dataset is obtained from the 1MW solar power plant of PDEU (our university), which has 12 structured columns and 1 unstructured column with manual text entries about different scheduled and unscheduled maintenance activities, and weather conditions on the daily basis. The unstructured column is used to create new features by using Hash-Map, flag words and stop words. The solar power generation forecasting is formulated as a vector auto regression (VAR) optimization problem and total power generation forecasting is presented with the results of four different cases. The results have shown that the root mean square percentage error (RMSPE) in total power generation forecasting is less than 10% for different lag (p) values. The vector auto regression can forecast the unscheduled maintenance activities like Grid failure, Inverter Failure, scheduled maintenance activity like module cleaning, weather activity like cloudy along with total power generation forecasting for effective and efficient management of solar power plants. The power generation decay is different for all the PV sets which show the variations in the impacts of weather, aging and maintenance on the solar power plant. This research work has proven that the peaks of total power generation forecasting and prediction can be tracked in a better way by using daily unscheduled, scheduled maintenance activities and weather conditions.
Introduction
Solar energy forecasting is an emerging area over the last decade by using historical time series data collected through a weather station (such as weather variables wind speed and direction, solar irradiance, and temperature). The scheduled and unscheduled maintenance activities are carried out frequently for efficient and effective management of solar power plant. Another important parameter that contributes slowly but effectively is the age of the Solar PV module. The degradation of PV modules is further impacted by the conditions such as High temperature, solar cell shedding, discoloration, and delamination, etc. India has been endowed with non-depleting renewable energy resources like solar, hydro, geothermal, and wind and it is currently ranked no. 3rd globally in the renewable energy market. According to MNRE sources, about 5,000 trillion kWh of energy is falling on India's land mass annually, with the majority receiving between 4 and 7 kWh per square meter per day. This is going to be one of the fastest-developing industries to boost rural electrification as well as the economy. Currently, there are more than 35 solar power plants in India. These plants are managed and operated by various government organizations. The operational expenditure of MW to GW solar photovoltaic power plants imposes a huge cost. Artificial Intelligence based solutions such as forecasting and recommendation-based systems can provide fruitful insights to reduce such expenditure.
Conclusions and Future Work
The maintenance activities like “grid failure”, “inverter failure”, “module cleaning” and the weather parameter “cloudy” have huge impact on the solar power generation forecasting and prediction. The optimum value of lag p=12 results in lowest RMSPE, RMSE and MAE for different cases. The error is reduced due to inclusion of duration for maintenance activities in this model for case 2. The VAR model used in this work is capable of forecasting all the four features as well as power generation target variable simultaneously. The predicted power generation is able to follow the trends of original power generation when the thresholding is applied to the predicted values of maintenance features (activities). The results have shown that the root mean square percentage error (RMSPE) in total power generation forecasting is less than 10% for different lag (p) values for all cases. The vector auto regression can forecast the unscheduled maintenance activities like Grid failure, Inverter Failure, scheduled maintenance activity like module cleaning, weather activity like cloudy along with total power generation forecasting which result in effective and efficient management of solar power plant. The power generation decay is different for all the PV sets which show the variation in the impacts of weather, aging and maintenance on the solar power plant. The effects of aging, dust and other environmental factors can be considered in the model as a future scope to improve the solar power generation forecasting and prediction.