This paper discusses the power quality issues for distributed generation systems based on renewable energy sources, such as solar and wind energy. A thorough discussion about the power quality issues is conducted here. This paper starts with the power quality issues, followed by discussions of basic standards. A comprehensive study of power quality in power systems, including the systems with dc and renewable sources is done in this paper. Power quality monitoring techniques and possible solutions of the power quality issues for the power systems are elaborately studied. Then, we analyze the methods of mitigation of these problems using custom power devices, such as D-STATCOM, UPQC, UPS, TVSS, DVR, etc., for micro grid systems. For renewable energy systems, STATCOM can be a potential choice due to its several advantages, whereas spinning reserve can enhance the power quality in traditional systems. At Last, we study the power quality in dc systems. Simpler arrangement and higher reliability are two main advantages of the dc systems though it faces other power quality issues, such as instability and poor detection of faults.

https://vbn.aau.dk/ws/files/294611667/08314672.pdf

Analysis and Mitigation of Power Quality Issues in Distributed Generation Systems Using Custom Power Devices

Renewable energy is essential for planet sustainability. Renewable energy output forecasting has a significant impact on making decisions related to operating and managing power systems. Accurate prediction of renewable energy output is vital to ensure grid reliability and permanency and reduce the risk and cost of the energy market and systems. Deep learning's recent success in many applications has attracted researchers to this field and its promising potential is manifested in the richness of the proposed methods and the increasing number of publications. To facilitate further research and development in this area, this paper provides a review of deep learning-based solar and wind energy forecasting research published during the last five years discussing extensively the data and datasets used in the reviewed works, the data pre-processing methods, deterministic and probabilistic methods, and evaluation and comparison methods. The core characteristics of all the reviewed works are summarised in tabular forms to enable methodological comparisons. The current challenges in the field and future research directions are given. The trends show that hybrid forecasting models are the most used in this field followed by Recurrent Neural Network models including Long Short-Term Memory and Gated Recurrent Unit, and in the third place Convolutional Neural Networks. We also find that probabilistic and multistep ahead forecasting methods are gaining more attention. Moreover, we devise a broad taxonomy of the research using the key insights gained from this extensive review, the taxonomy we believe will be vital in understanding the cutting-edge and accelerating innovation in this field.

A review and taxonomy of wind and solar energy forecasting methods based on deep learning

An improved residual-based convolutional neural network for very short-term wind power forecasting

As the result of increasing population and growing technological activities, nonrenewable energy sources, which are the main energy providers, are diminishing day by day. Due to this factor, efforts on efficient utilization of renewable energy sources have increased all over the world. Wind is one of the most significant alternative energy resources. However, in comparison with other renewable energy sources, it is so variable that there is a need for estimating and planning of wind power generation. In this paper, a new statistical short-term (up to 48 h) wind power forecast model, namely statistical hybrid wind power forecast technique (SHWIP), is presented. In the proposed model, weather events are clustered with respect to the most important weather forecast parameters. It also combines the power forecasts obtained from three different numerical weather prediction (NWP) sources and produces a hybridized final forecast. The proposed model has been in operation at the Wind Power Monitoring and Forecast System for Turkey (RITM), and the results of the new model are compared with well-known statistical models and physical models in the literature. The most important characteristics of the proposed model is the need for a lesser amount of historical data while constructing the mathematical model compared with the other statistical models such as artificial neural networks (ANN) and support vector machine (SVM). To produce a reliable forecast, ANN and SVM need at least 1 year of historical data; on the other hand, the proposed SHWIP method’s results are applicable even under 1 month of training data, and this is an important feature for the forecast of the newly established wind power plants (WPPs).

A Novel Wind Power Forecast Model: Statistical Hybrid Wind Power Forecast Technique (SHWIP)

This paper presents a novel application of the metaheuristic Slime Mould Algorithm (SMA) to the optimal tuning of interval type-2 fuzzy controllers. Inserting the information feedback model F1 in S...

Optimal tuning of interval type-2 fuzzy controllers for nonlinear servo systems using Slime Mould Algorithm

Wind energy conversion systems (WECS) which consist of wind turbines with permanent magnet synchronous generator (PMSG) and full-power converters have become widespread in the field of renewable power systems. Generally, conventional diode bridge rectifiers have used to obtain a constant DC bus voltage from output of PMSG based wind generator. In recent years, together advanced power electronics technology, Pulse Width Modulation (PWM) rectifiers have used in WECS. PWM rectifiers are used in many applications thanks to their characteristics such as high power factor and low harmonic distortion. In general, L, LC and LCL-type filter configurations are used in these rectifiers. These filter configurations are not exactly compensate current and voltage harmonics. This study proposes a hybrid passive filter configuration for PWM rectifiers instead of existing filters. The performance of hybrid passive filter was tested via MATLAB/Simulink environment under various operational conditions and was compared with LCL filter structure. In addition, neuro-fuzzy controller (NFC) was preferred to increase the performance of PWM rectifier in DC bus voltage control against disturbances because of its robust and nonlinear structure. The study demonstrates that the hybrid passive filter configuration proposed in this study successfully compensates current and voltage harmonics, and improves total harmonic distortion and true power factor.

Power Quality Improvement Using Hybrid Passive Filter Configuration for Wind Energy Systems

During the last decades, thanks to supportive policies of countries and a decrease in installation costs, total installed capacity of wind power has increased rapidly all around the world. The uncertain and variable nature of wind power has been a problem for transmission system operators and wind power plant owners. To solve this problem, numerous wind power forecast systems have been developed. Unfortunately none of them can obtain absolutely accurate forecasts yet. Thus, researchers assumed that wind power generation is a stochastic process and they proposed a stochastic programming approach to solve problems arising from the uncertainty of wind power. It is well known that representing stochastic process by possible scenarios is a major issue in the stochastic programming approach. Large numbers of scenarios can represent a stochastic process accurately, but it is not easy to solve a stochastic problem that contains a large number of scenarios. For this reason scenario reduction methods have been introduced. Finally, the quality of this reduced scenario set must be at an acceptable level to use them in calculations. All of these reasons have encouraged authors to develop a wind power scenario tool that can generate and reduce the scenario set and test the quality of it. The developed tool uses historical data to model wind forecast errors. Scenarios are generated around 24 day-ahead point wind power forecasts. A fast forward reduction algorithm is used to reduce the scenario set. Two metrics are proposed to assess the quality of the reduced scenario set. Site measurements are used to test the developed wind power scenario tool. Results showed that the tool can generate and reduce the scenario set successfully and the proposed metrics are useful to assess the quality.

/pdf/a-day-ahead-wind-power-scenario-generation-reduction-and-3znresgbbe.pdf

A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool

In this study, wind power forecasting is performed for a Wind Power Plant (WPP) with an installed capacity of 135 MW in Turkey. The ruggedness index (RIX) of the terrain where WPP was installed is ...

An extreme learning machine based very short-term wind power forecasting method for complex terrain

In this study, a novel photovoltaic power forecasting system that utilizes a deep Convolutional Neural Network (CNN) structure and an input signal decomposition algorithm is proposed The proposed 

Ceyhun Yildiz

Papers

An improved residual-based convolutional neural network for very short-term wind power forecasting

Power Quality Improvement Using Hybrid Passive Filter Configuration for Wind Energy Systems

A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool

An extreme learning machine based very short-term wind power forecasting method for complex terrain

A Novel Short-Term Photovoltaic Power Forecasting Approach based on Deep Convolutional Neural Network