A power balance simulator to examine business continuity in hospital facilities due to power outages in a disaster

2021 ◽  
pp. 1-21
Author(s):  
Akane Uemichi ◽  
Ryo Oikawa ◽  
Yudai Yamasaki ◽  
Shigehiko Kaneko
Keyword(s):  
Power Quality ◽  
Moving Average ◽  
Weather Conditions ◽  
Frequency Fluctuation ◽  
Feedback Gain ◽  
Power Outages ◽  
Photovoltaic Panels ◽  
Gas Engine ◽  
Hospital Building ◽  
Storage Batteries

Abstract In hospitals, the energy supply is the key to ensuring modern medical care even during power outages due to a disaster. This study qualitatively examined whether the supply-demand balance can be stabilized by the private generator prepared by the hospital building during stand-alone operations under disaster conditions. In the nanogrid of the hospital building, the power quality was examined based on the AC frequency, which characterizes the supply-demand balance. Gas engine generators, emergency diesel generators, photovoltaic panels, and storage batteries were presumed to be the private generators in the hospital building. The output reference values for the emergency diesel and gas engine generators were set using droop control, and the C/D controller enabled synchronized operation. In addition, to keep the AC frequency fluctuation minor, the photovoltaic panels were designed to suppress the output fluctuation using storage batteries. As a result of case studies, the simulator predicts that the frequency fluctuation varies greatly depending on the weather conditions and the fluctuation suppression parameters, even for the same configuration with the same power generation capacity. Therefore, it is preferable to increase the moving average time of the output and reduce the feedback gain of the storage battery to suppress the output fluctuation from the photovoltaics. However, there is a tradeoff between suppressing the output fluctuation and the minimum required storage capacity. Furthermore, since the photovoltaics' output varies with the weather, other private generators' capacity and control parameters significantly impact power quality. The simulator proposed in this study makes it possible to study each hospital's desirable private generator configuration.

scholarly journals The Use of Drone Photo Material to Classify the Purity of Photovoltaic Panels Based on Statistical Classifiers

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 483
Author(s):  
Tomasz Czarnecki ◽  
Kacper Bloch
Keyword(s):  
Renewable Energy Sources ◽  
Base Material ◽  
Weather Conditions ◽  
High Rate ◽  
Solar Panels ◽  
Soil Dust ◽  
Photovoltaic Panels ◽  
Statistical Classifiers ◽  
Study Results ◽  
The Subject

The subject of this work is the analysis of methods of detecting soiling of photovoltaic panels. Environmental and weather conditions affect the efficiency of renewable energy sources. Accumulation of soil, dust, and dirt on the surface of the solar panels reduces the power generated by the panels. This paper presents several variants of the algorithm that uses various statistical classifiers to classify photovoltaic panels in terms of soiling. The base material was high-resolution photos and videos of solar panels and sets dedicated to solar farms. The classifiers were tested and analyzed in their effectiveness in detecting soiling. Based on the study results, a group of optimal classifiers was defined, and the classifier selected that gives the best results for a given problem. The results obtained in this study proved experimentally that the proposed solution provides a high rate of correct detections. The proposed innovative method is cheap and straightforward to implement, and allows use in most photovoltaic installations.

scholarly journals Forecasting and Modelling of Solar Radiation for Photovoltaic (PV) Systems

2021 ◽  
Author(s):  
Ines Sansa ◽  
Najiba Mrabet Bellaaj
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
Cloud Cover ◽  
Moving Average ◽  
Arma Model ◽  
Weather Conditions ◽  
Pv Systems ◽  
Auto Regressive ◽  
Micro Grid ◽  
One Year

Solar radiation is characterized by its fluctuation because it depends to different factors such as the day hour, the speed wind, the cloud cover and some other weather conditions. Certainly, this fluctuation can affect the PV power production and then its integration on the electrical micro grid. An accurate forecasting of solar radiation is so important to avoid these problems. In this chapter, the solar radiation is treated as time series and it is predicted using the Auto Regressive and Moving Average (ARMA) model. Based on the solar radiation forecasting results, the photovoltaic (PV) power is then forecasted. The choice of ARMA model has been carried out in order to exploit its own strength. This model is characterized by its flexibility and its ability to extract the useful statistical properties, for time series predictions, it is among the most used models. In this work, ARMA model is used to forecast the solar radiation one year in advance considering the weekly radiation averages. Simulation results have proven the effectiveness of ARMA model to forecast the small solar radiation fluctuations.

Modeling the PV Power Systems Storage Batteries Charging Characteristics

2017 ◽  
Vol 0 (0) ◽  
Author(s):  
E. M. Diaconu
Keyword(s):  
Mathematical Model ◽  
Power Systems ◽  
Numerical Method ◽  
Interpolation Method ◽  
Source Code ◽  
Photovoltaic Panels ◽  
Data Acquisition Board ◽  
Code Algorithm ◽  
Storage Batteries ◽  
Theoretical Results

Abstract In this paper, a numerical method is presented for measuring and analyzing the characteristics of the charging process for nine photovoltaic panels storage batteries systems. The data was collected with the PV charger module which has a built-in data acquisition board. A source code algorithm written in Matlab is developed to obtain a mathematical model for the characteristics of the charging process of the batteries connected to the PV panels. Using the interpolation method, a mathematical model is obtained. The numerical error between experimental and theoretical results prove that the method is accurate.

scholarly journals Fourier Analysis for Demand Forecasting in a Fashion Company

10.5772/56839 ◽  
2013 ◽  
Vol 5 ◽  
pp. 30 ◽  
Author(s):  
Andrea Fumi ◽  
Arianna Pepe ◽  
Laura Scarabotti ◽  
Massimiliano M. Schiraldi
Keyword(s):  
Moving Average ◽  
Demand Forecasting ◽  
Fourier Method ◽  
Weather Conditions ◽  
Economic Factors ◽  
Fast Fourier Transform Algorithm ◽  
Fashion Industry ◽  
Forecasting Method ◽  
Short Lifecycle ◽  
Socio Economic Factors

In the fashion industry, demand forecasting is particularly complex: companies operate with a large variety of short lifecycle products, deeply influenced by seasonal sales, promotional events, weather conditions, advertising and marketing campaigns, on top of festivities and socio-economic factors. At the same time, shelf-out-of-stock phenomena must be avoided at all costs. Given the strong seasonal nature of the products that characterize the fashion sector, this paper aims to highlight how the Fourier method can represent an easy and more effective forecasting method compared to other widespread heuristics normally used. For this purpose, a comparison between the fast Fourier transform algorithm and another two techniques based on moving average and exponential smoothing was carried out on a set of 4-year historical sales data of a €60+ million turnover medium- to large-sized Italian fashion company, which operates in the women's textiles apparel and clothing sectors. The entire analysis was performed on a common spreadsheet, in order to demonstrate that accurate results exploiting advanced numerical computation techniques can be carried out without necessarily using expensive software.

scholarly journals Influence of Measurement Aggregation Algorithms on Power Quality Assessment and Correlation Analysis in Electrical Power Network with PV Power Plant

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3547 ◽  
Author(s):  
Jasiński ◽  
Sikorski ◽  
Kostyła ◽  
Kaczorowska ◽  
Leonowicz ◽  
...  
Keyword(s):  
Power Plant ◽  
Correlation Analysis ◽  
Quality Assessment ◽  
Power Quality ◽  
Low Voltage ◽  
Electrical Power ◽  
Weather Conditions ◽  
Comparative Investigation ◽  
Quality Parameters ◽  
Voltage Variation

Recently a number of changes were introduced in amendment to standard EN 50160 related to power quality (PQ) including 1 min aggregation intervals and the obligation to consider 100% of measured data taken for the assessment of voltage variation in a low voltage (LV) supply terminal. Classical power quality assessment can be extended using a correlation analysis so that relations between power quality parameters and external indices such as weather conditions or power demand can be revealed. This paper presents the results of a comparative investigation of the application of 1 and 10 min aggregation times in power quality assessment as well as in the correlation analysis of power quality parameters and weather conditions and the energy production of a 100 kW photovoltaic (PV) power plant connected to a LV network. The influence of the 1 min aggregation time on the result of the PQ assessment as well as the correlation matrix in comparison with the 10 min aggregation algorithm is presented and discussed.

А System for Monitoring the Number and Duration of Power Outages and Power Quality in 0.38 kV Electrical Networks

2018 ◽  
pp. 1-10 ◽  
Author(s):  
Alexander Vinogradov ◽  
Vadim Bolshev ◽  
Alina Vinogradova ◽  
Tatyana Kudinova ◽  
Maksim Borodin ◽  
...  
Keyword(s):  
Power Quality ◽  
Electrical Networks ◽  
Power Outages

scholarly journals Multi-step-ahead forecasting of hourly potential evapotranspiration for irrigation triggering in horticultural nurseries under oceanic climate

2021 ◽  
Vol 958 (1) ◽  
pp. 012025
Author(s):  
R Tawegoum
Keyword(s):  
Potential Evapotranspiration ◽  
Moving Average ◽  
Recursive Algorithm ◽  
Weather Conditions ◽  
Irrigation Scheduling ◽  
Model Parameters ◽  
Sensor Failure ◽  
Climate Data ◽  
Local Climate ◽  
Climate Conditions

Abstract Predicting hourly potential evapotranspiration is particularly important in constrained horticultural nurseries. This paper presents a three-step-ahead predictor of potential evapotranspiration for horticultural nurseries under unsettled weather conditions or climate sensor failure. The Seasonal AutoRegressive Integrated Moving Average model based on climate data was used to derive a predictor using data generated according to prior knowledge of the system behavior; the aim of the predictor was to compensate for missing data that are usually not considered in standard forecasting approaches. The generated data also offer the opportunity to capture variations of the model parameters due to abrupt changes in local climate conditions. A recursive algorithm was used to estimate parameter variation, and the Kalman filter to model the state of the system. The simulations for steady-state weather and unsettled weather conditions showed that the predictor could forecast potential evapotranspiration more accurately than the standard approach did. These results are encouraging within the context of predictive irrigation scheduling in nurseries.

scholarly journals Prediksi Cuaca Kota Surabaya Menggunakan Autoregressive Integrated Moving Average (Arima) Box Jenkins dan Kalman Filter

2018 ◽  
Vol 4 (1) ◽  
pp. 59-67
Author(s):  
Nurissaidah Ulinnuha ◽  
Yuniar Farida
Keyword(s):  
Kalman Filter ◽  
Moving Average ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Autoregressive Integrated Moving Average ◽  
Wind Speed Prediction ◽  
The People ◽  
Speed Prediction ◽  
The Impact

Season changes conditions in Indonesia cause many disasters such as landslides, floods and whirlwinds and even hail. Extreme weather conditions that occur, it is better to remain alert to anticipate the various possibilities that occur and to reduce and minimize the impact that can harm the people. The design of weather prediction system in this research using Autoregressive Integrated Moving Average ARIMA Box Jenkins model and Kalman filter with the aim to predict the increasingly extreme weather of Surabaya city at the end of 2017. In this research, weather prediction focused on humidity, temperature, and velocity wind with results 5 days later. The prediction of Surabaya city weather using ARIMA method - Kalman filter obtained the smallest error goal (error MAPE) of 0.000014 each for the prediction of humidity, 0.000037 for temperature prediction, and 0.0123 for wind speed prediction.

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