ANFIS-based prediction of power generation for combined cycle power plant

This paper presents the application of an adaptive neuro-fuzzy inference system (ANFIS) to predict the generated electrical power in a combined cycle power plant. The ANFIS architecture is implemented in MATLAB through a code that utilizes a hybrid algorithm that combines gradient descent and the least square estimator to train the network. The Model is verified by applying it to approximate a nonlinear equation with three variables, the time series Mackey-Glass equation and the ANFIS toolbox in MATLAB. Once its validity is confirmed, ANFIS is implemented to forecast the generated electrical power by the power plant. The ANFIS has three inputs: temperature, pressure, and relative humidity. Each input is fuzzified by three Gaussian membership functions. The first-order Sugeno type defuzzification approach is utilized to evaluate a crisp output. Proposed ANFIS is cable of successfully predicting power generation with extremely high accuracy and being much faster than Toolbox, which makes it a promising tool for energy generation applications

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

Alkire-Foster oriented ensemble fuzzy inference system for urban poverty classification

Malaysia is a developing country which relies on the monetary approach to measure poverty. The approach is simple to measure but it is insensitive towards changes of the poor in multiple dimensions such as education, health and living standards especially in urban areas. Several current issues in classifying the urban poor include rigid dichotomy of the poor and non-poor, unable to capture changes that happens in various sub-groups of urban poor population and misclassified poverty indicators. This study developed a multidimensional poverty measurement framework which integrated i) Alkire-Foster approaches in quantification of multidimensional urban poor, ii) Adaptive Neural Fuzzy Inference Systems (ANFIS) to predict classification of urban poor and resolve the misclassification of urban poor and iii) ensemble ANFIS. 300 questionnaires were distributed to targeted households in Bandar Tasik Selatan, Kuala Lumpur. This study started with a comparison of datadriven Fuzzy Rule-Based System (FRBS) with the domain expert comprising FRBS classification. Next, the Alkire-Foster method was introduced which included parameter selection, dual cut off identification and aggregation of the poor. Then, the ANFIS prediction was carried out using various ANFIS combination models such as Genfis 1, Genfis 2 and Genfis 3 to predict the classification of urban poor. This study proceeded to improve the classification by proposing the ensemble ANFIS that included ensemble weighting and ensemble integration method. The performance of this proposed framework was evaluated using Root Mean Square Error (RMSE), Mean Square Error (MSE), and R-Squared. For validation purposes, this study was reviewed by officers at the Zakat Collection Centre, Kuala Lumpur as the domain experts. The findings showed that the Genfis 3 using Fuzzy C-Means clustering algorithm in ANFIS outperformed all the ANFIS models, by obtaining the least MSE and RMSE values and highest R-Squared. These results included the Health dimension which was excluded in the current poverty measurement. Overall, this study has managed to address the urban poor classification by providing multiple dimensions of the poor and produce robust prediction results

Modelling of an electro-hydraulic actutor using extended adaptive distance gap statistic approach

The existence of high degree of non-linearity in Electro-Hydraulic Actuator (EHA) system has imposed a challenging task in developing its model so that effective control algorithm can be proposed. In general, there are two modelling approaches available for EHA system, which are the dynamic equation modelling method and the system identification modelling method. Both approaches have disadvantages, where the dynamic equation modelling is hard to apply and some parameters are difficult to obtain, while the system identification method is less accurate when the system’s nature is complicated with wide variety of parameters, nonlinearity and uncertainties. This thesis presents a new modelling procedure of an EHA system by using fuzzy approach. Two sets of input variables are obtained, where the first set of variables are selected based on mathematical modelling of the EHA system. The reduction of input dimension is done by the Principal Component Analysis (PCA) method for the second set of input variables. A new gap statistic with a new within-cluster dispersion calculation is proposed by introducing an adaptive distance norm in distance calculation. The new gap statistic applies Gustafson Kessel (GK) clustering algorithm to obtain the optimal number of cluster of each input. GK clustering algorithm also provides the location and characteristic of every cluster detected. The information of input variables, number of clusters, cluster’s locations and characteristics, and fuzzy rules are used to generate initial Fuzzy Inference System (FIS) with Takagi-Sugeno type. The initial FIS is trained using Adaptive Network Fuzzy Inference System (ANFIS) hybrid training algorithm with an identification data set. The ANFIS EHA model and ANFIS PCA model obtained using proposed modelling procedure, have shown the ability to accurately estimate EHA system’s performance at 99.58% and 99.11% best fitting accuracy compared to conventional linear Autoregressive with External Input (ARX) model at 94.97%. The models validation result on different data sets also suggests high accuracy in ANFIS EHA and ANFIS PCA model compared to ARX model

Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market

The development of artificial intelligence (AI) based techniques for electricity price forecasting (EPF) provides essential information to electricity market participants and managers because of its greater handling capability of complex input and output relationships. Therefore, this research investigates and analyzes the performance of different optimization methods in the training phase of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for the accuracy enhancement of EPF. In this work, a multi-objective optimization-based feature selection technique with the capability of eliminating non-linear and interacting features is implemented to create an efficient day-ahead price forecasting. In the beginning, the multi-objective binary backtracking search algorithm (MOBBSA)-based feature selection technique is used to examine various combinations of input variables to choose the suitable feature subsets, which minimizes, simultaneously, both the number of features and the estimation error. In the later phase, the selected features are transferred into the machine learning-based techniques to map the input variables to the output in order to forecast the electricity price. Furthermore, to increase the forecasting accuracy, a backtracking search algorithm (BSA) is applied as an efficient evolutionary search algorithm in the learning procedure of the ANFIS approach. The performance of the forecasting methods for the Queensland power market in the year 2018, which is well-known as the most competitive market in the world, is investigated and compared to show the superiority of the proposed methods over other selected methods.© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed