A Review of Classification Problems and Algorithms in Renewable Energy Applications

Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field

Probabilistic Framework for Online Identification of Dynamic Behavior of Power Systems with Renewable Generation

The paper introduces a probabilistic framework for online identification of post fault dynamic behavior of power systems with renewable generation. The framework is based on decision trees and hierarchical clustering and incorporates uncertainties associated with network operating conditions, topology changes, faults, and renewable generation. In addition to identifying unstable generator groups, the developed clustering methodology also facilitates identification of the sequence in which the groups lose synchronism. The framework is illustrated on a modified version of the IEEE 68 bus test network incorporating significant portion of renewable generation

Comparative Analysis of Building Insurance Prediction Using Some Machine Learning Algorithms

In finance and management, insurance is a product that tends to reduce or eliminate in totality or partially the loss caused due to different risks. Various factors affect house insurance claims, some of which contribute to formulating insurance policies including specific features that the house has. Machine Learning (ML) when brought into the field of insurance would enable seamless formulation of insurance policies with a better performance which will also save time. Various classification algorithms have been used since they have a long history and have also got some modifications for optimum functionality. To illustrate the performance of each of the ML algorithms that we used here, we analyzed an insurance dataset drawn from Zindi Africa competition which is said to be from Olusola Insurance Company in Lagos Nigeria. This study therefore, compares the performance of Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), Kernel Support Vector Machine (kSVM), Naïve Bayes (NB), and Random Forest (RF) Regressors on a dataset got from Zindi.africa competition and their performances are checked using not only accuracy and precision metrics but also recall, and F1 score metrics, all displayed on the confusion matrix. The accuracy result shows that logistic regression and Kernel SVM both gave 78% but kSVM outperformed LR in precision with a percentage of 70.8% for kSVM and 64.8% for LR showing that kSVM offered the best result

An intelligent fault diagnosis method for PV arrays based on an improved rotation forest algorithm

With the exponential growth of global photovoltaic (PV) power capacity, it is essential to monitor, detect and diagnose the faults in PV arrays for optimal operation. This paper presents an improved rotation forest (RoF) algorithm classifiers ensemble hybridized with extreme learning machine (ELM) for fault diagnosis of PV arrays, which mainly consists of feature selection and classification. In the feature selection step, all the attributes are ranked by the ReliefF algorithm and the top-ranked attributes are chosen to create the new training data subset. In the classification step, the base classifier decision tree of the RoF is replaced by the extreme learning machine to form a new hybrid RoF-ELM ensemble classifier. In the RoF-ELM algorithm, the feature space is first split into several subspaces and the best number of feature subsets is found through the traversal search method. Then, the bootstrap algorithm is employed to carry out bootstrap resampling for each feature subspace, and the principal component analysis (PCA) is then used to transform the resampled samples. Finally, the ELM base classifier is exploited to build each classification model and the final decision is determined by the simple voting approach. By combining the RoF ensemble method with the ELM classifier, the proposed RoF-ELM algorithm not only overcomes the overfitting problem of the basic RoF algorithm, but also improves the generalization ability of the basic ELM. In order to experimentally verify the proposed approach, different types and levels of faults have been created in a laboratory small scale grid-connected PV power system to obtain the fault data samples. Experimental results demonstrate that the RoF-ELM can achieve higher diagnosis accuracy and reliability compared to the basic RoF and ELM algorithms

Aurinkosähkövaihtosuuntaajan tilastollinen lämpötilan estimointi

The purpose of this work is to understand whether a broken temperature sensor can be identified from time series data, if a probabilistic temperature model can be formulated for a single measurement for an outdoor inverter, and whether the inverter can continue converting power under the probabilistic model if the sensor is broken. Data given for this study were acquired from different experiments during the design and verification of a 2-MW outdoor central inverter for large utility-scale PV power plants. Based on these objectives, probabilistic methodology was constructed to identify outliers in the data, simulate very short-term temperature time series, and evaluate whether a certain temperature threshold is exceeded as a safety measure for continuing inverter operation. The proposed model is constructed of two blocks: an outlier detection block and an estimation block. The first block is based on principal component analysis, K-means and elliptical density estimation. The second block is based on Markov chain. The proposed methodology uses temperature time series data only without knowing the internals of the system. The proposed model was validated by inputting time-series data containing data from faulty temperature sensors under different failure scenarios, and by comparing simulated temperature time series data to historical temperature data under different cases. Moreover, the simulated time series data were used to verify whether the model can anticipate exceeding a certain temperature threshold. The model always detected the failed sensors. The error metrics of the simulated temperature time series were low. Furthermore, the model anticipated exceeding the given temperature threshold ahead of time.Tämän diplomityön tarkoituksena on tutkia mahdollisuutta tunnistaa vaihtosuuntaajan vioittunut lämpötila-anturi lämpötila-aikasarjoista, muodostaa tilastollinen malli yhdelle lämpötilamittaukselle sekä arvioida, voidaanko vaihtosuuntaajan toimintaa jatkaa tilastollisen mallin avulla lämpötila-anturin vioittuessa. Materiaalina käytettiin suuriin aurinkovoimaloihin suunnitellun 2 MW:n keskusinvertterin erilaisista kokeista kerättyjä lämpötilamittauksia. Työn tavoitteiden pohjalta muodostettiin tilastollinen menetelmä, joka tunnistaa vioittuneen lämpötila-anturin, simuloi lyhytaikaisia lämpötila-aikasarjoja sekä ennustaa vaihtosuuntaajan toiminnan jatkamisen kannalta, ylittyykö ennalta-asetettu lämpötilaraja. Esitetty malli on rakennettu vioittuneen lämpötila-anturin tunnistavasta lohkosta ja lämpötilaa estimoivasta lohkosta. Ensimmäinen lohko perustuu pääkomponenttianalyysiin, K:n keskiarvon klusterointimenetelmään ja virhe-ellipsiin. Toinen lohko perustuu Markovin ketjuun. Esitetty malli käyttää lähtötietona vain aikaisempia lämpötila-aikasarjoja. Menetelmän toimivuutta tutkittiin ensin tunnistamalla viallinen lämpötila-anturi sekä vertaamalla estimoitujen lämpötila-aikasarjojen jakaumia historiallisiin lämpötilatietoihin erilaisissa vioittumistapauksissa. Lisäksi menetelmän kykyä ennakoida ennalta-asetetun lämpötilarajan ylittämistä tutkittiin eri esimerkkien avulla. Esitetty menetelmä havaitsi vioittuneet lämpötila-anturit poikkeuksetta. Ennustettujen ja havaittujen lämpötila-aikasarjojen väliset erot olivat hyvin pieniä. Malli pystyi myös ennakoimaan tietyn lämpötilarajan ylittymisen

Solar Energy Prediction Model Based on Artificial Neural Networks and Open Data

With climate change driving an increasingly stronger influence over governments and municipalities, sustainable development, and renewable energy are gaining traction across the globe. This is reflected within the EU 2030 agenda, that envisions a future where there is universal access to affordable, reliable and sustainable energy. One of the challenges to achieve this vision lies on the low reliability of certain renewable sources. While both particulars and public entities try to reach self-sufficiency through sustainable energy generation, it is unclear how much investment is needed to mitigate the unreliability introduced by natural factors such as varying wind speed and daylight across the year. In this sense, a tool that aids predicting the energy output of sustainable sources across the year for a particular location can aid greatly in making sustainable energy investments more efficient. In this paper, we make use of Open Data sources, Internet of Things (IoT) sensors and installations distributed across Europe to create such tool through the application of Artificial Neural Networks. We analyze how the different factors affect the prediction of energy production and how Open Data can be used to predict the expected output of sustainable sources. As a result, we facilitate users the necessary information to decide how much they wish to invest according to the desired energy output for their particular location. Compared to state-of-the-art proposals, our solution provides an abstraction layer focused on energy production, rather that radiation data, and can be trained and tailored for different locations using Open Data. Finally, our tests show that our proposal improves the accuracy of the forecasting, obtaining a lower mean squared error (MSE) of 0.040 compared to an MSE 0.055 from other proposals in the literature.This paper has been co-funded by the ECLIPSE-UA (RTI2018-094283-B-C32) project from the Spanish Ministry of Science, Innovation, and Universities; both Jose M. Barrera (I-PI 98/18) and Alejandro Reina (I-PI 13/20) hold an Industrial PhD Grants co-funded by the University of Alicante and the Lucentia Lab Spin-off Company

The Impact of Transmission Protection System Reliability on Power System Resilience

Power transmission operation regimes are being changed for various technical and economic reasons seeking an improved power system resilience as a goal. However, some of these changes introduce new challenges in maintaining conventional transmission protection system dependability and security when meeting the operating complexities affecting power system resilience. Frequently evolving network topology, as a result of multiple switching actions for corrective, predictive and post event purposes, as well as high penetration of distributed generation into the system are considered as major contradictory changes from the legacy transmission protection standpoint. This research investigates the above-mentioned challenges and proposes effective solutions to improve the transmission protection reliability facing the above-mentioned risks and power system resilience consequently. A fundamental protection scheme based on the Hierarchically Coordinated Protection (HCP) concept is proposed to illustrate various approaches to predictive, adaptive and corrective protection actions aimed at improving power system resilience. Novel computation techniques as well as intelligent machine-learning algorithms are employed in proposing predictive, adaptive, and corrective solutions which fit various layers of the HCP concept and incorporate a fundamental HCP-based approach to supervise the legacy transmission protection function for a dynamic balance between dependability and security. The proposed predictive, adaptive, and corrective protection approaches are tested and verified on various systems, including real-life and IEEE test systems, and their performance effectiveness is compared with the state of the art

Application of artificial intelligence in fault detection and classification of solar power plants and prediction of power generation of combined cycled power plants

Solar energy is one of the most dependable renewable energy technologies, as it is feasible almost everywhere globally and is environmentally friendly. Photovoltaic-based renewable energy systems are highly susceptible to power grid transients. Their operation may suffer drastically during faults in the solar arrays, power electronics, and the inverter. Thus, it is vital to develop an intelligent mechanism to detect any type of fault or abnormalities within the shortest possible time that will increase reliability and decrease the maintenance cost of the solar system. To accomplish that, in this research, different artificial intelligence (AI) techniques are utilized to develop classification, fault detection, and optimization algorithms for solar photovoltaic (PV) panels. Initially, a convolutional neural network (CNN) model was designed to detect and classify PV modules based on the images taken from the solar panels. It is found that the proposed CNN model can identify the fault with an accuracy of 91.1% for binary (i.e., healthy and faulty) and 88.6% for multi-classification (i.e. cracked, shadowy, dusty and normal). However, sometimes the fault in the solar panel may not be detectable from the images of the solar panels. That is why an adaptive neuro-fuzzy inference system (ANFIS) model is developed to detect and classify the defects of PV systems based on the signals collected from the solar panels. The performance of the developed defect detection and classification algorithms was tested using real-life solar farm datasets. The performance of the proposed ANFIS-based fault detection scheme has been compared with different machine learning algorithms. It is found from the comparative results that the proposed ANFIS-based fault detection technique is robust and straightforward. Thus, the developed ANFISbased intelligent technique will enhance the reliability of the PV system by minimizing the maintenance cost and saving energy. Finally, another ANFIS model is developed to predict the power generation in a combined cycle power plant. The codes were written in MATLAB, and their validity is confirmed with the available ANFIS toolboxes in MATLAB. The proposed ANFIS is found capable of successfully predicting power generation with extremely high accuracy and being much faster than the built-in ANFIS of MATLAB Toolbox. Thus, the developed ANFIS model could be utilized as a promising tool for energy generation applications

Smart Distributed Generation System Event Classification using Recurrent Neural Network-based Long Short-term Memory

High penetration of distributed generation (DG) sources into a decentralized power system causes several disturbances, making the monitoring and operation control of the system complicated. Moreover, because of being passive, modern DG systems are unable to detect and inform about these disturbances related to power quality in an intelligent approach. This paper proposed an intelligent and novel technique, capable of making real-time decisions on the occurrence of different DG events such as islanding, capacitor switching, unsymmetrical faults, load switching, and loss of parallel feeder and distinguishing these events from the normal mode of operation. This event classification technique was designed to diagnose the distinctive pattern of the time-domain signal representing a measured electrical parameter, like the voltage, at DG point of common coupling (PCC) during such events. Then different power system events were classified into their root causes using long short-term memory (LSTM), which is a deep learning algorithm for time sequence to label classification. A total of 1100 events showcasing islanding, faults, and other DG events were generated based on the model of a smart distributed generation system using a MATLAB/Simulink environment. Classifier performance was calculated using 5-fold cross-validation. The genetic algorithm (GA) was used to determine the optimum value of classification hyper-parameters and the best combination of features. The simulation results indicated that the events were classified with high precision and specificity with ten cycles of occurrences while achieving a 99.17% validation accuracy. The performance of the proposed classification technique does not degrade with the presence of noise in test data, multiple DG sources in the model, and inclusion of motor starting event in training samples

State of the art of machine learning models in energy systems: A systematic review

Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability