Search

Accurate photovoltaic (PV) power forecasting is indispensable to enhancing the stability of the power grid and expanding the absorptive photoelectric capacity of the power grid. As an excellent nonlinear regression model, the relevance vector machine (RVM) can be employed to forecast PV power. However, the optimization of the free parameters is still a key problem for improving the performance of the RVM. Taking advantage of the strong global search capability, good stability, and fast convergence rate of the sparrow search algorithm (SSA), this paper optimizes the parameters of the RVM by using the SSA to develop an excellent RVM (called SSA-RVM). Consequently, a novel hybrid PV power forecasting model via the SSA-RVM is proposed to perform ultra-short-term (4 h ahead) prediction. In addition, the effects of seasonal distribution and weather type on PV power are fully considered, and different seasonal prediction models are established separately to improve the prediction capability. The benchmark is used to verify the accuracy of the SSA-RVM-based forecasting model under various conditions, and the experiment results demonstrate that the proposed SSA-RMV method outperforms the traditional RVM and support vector machine models, and it even shows a better prediction effect than the RVM models with other optimization approaches. [ABSTRACT FROM AUTHOR]

In the wake of the United Nation's Sustainable Development Goals—zero hunger and affordable modern/clean energy for all—many developing countries have taken serious steps in recent years to increase clean energy access for the rural population. The government of Pakistan has similarly made numerous efforts to promote the use of clean energy sources in the rural areas of the country. Therefore, this study examines rural households' energy choices for cooking and lighting in Pakistan. In doing so, a comprehensive dataset is collected from three different districts of Pakistan between 2020 and 2021, and multivariate probit (MVP) model and Chi-square tests are employed. The Chi-square results indicate that the age, education level, and occupation of the household-head; household size and income; distance to market and wood source; and biogas system ownership are the significant factors affecting cooking choices. The MVP results show that an increase in education level, school-going children, access to credit facilities, and gender (female) are the key positive factors, whereas an increase in the distance to nearest market/road, household size, and age are the factors that negatively affect the likelihood of using clean energy sources for lighting. While comparing the propensity to use modern/clean energy fuels across the three districts, infrastructural development and literacy rate were found to be crucial factors. [ABSTRACT FROM AUTHOR]

The main objective of the current work was to enhance the transformer fault diagnostic accuracy based on dissolved gas analysis (DGA) data with a proposed coupled system of support vector machine (SVM)-bat algorithm (BA) and Gaussian classifiers. Six electrical and thermal fault classes were categorized based on the IEC and IEEE standard rules. The concentration of five main combustible gases (hydrogen, methane, ethane, ethylene, and acetylene) was utilized as an input vector of the two classifiers. Two types of input vectors have been tested; the first input type considered the five gases in ppm, and the second input type considered the gases introduced in the percentage of the sum of the five gases. An extensive database of 481 had been used for training and testing phases (321 data samples for training and 160 data samples for testing). The SVM model conditioning parameter "λ" and penalty margin parameter "C" were adjusted through the bat algorithm to develop a maximum accuracy rate. The SVM-BA and Gaussian classifiers' accuracy was evaluated and compared with several DGA techniques in the literature. [ABSTRACT FROM AUTHOR]

The aging of power transformers causes several defects and damages in the insulating system, especially in the insulating paper. The degradation of the insulating paper generates dissolved gases in the insulating oil, which are measured by gas chromatography and used as an indicator of the insulation status. The state of the insulating paper can be identified based on the degree of polymerization (DP) measurement. In some cases, when the measurement of DP is difficult, estimating DP can be accomplished through gathering information about some of the testing parameters, such as the dissolved gases (DGA), breakdown voltage (BDV), oil interfacial tension (IF), oil acidity (ACI), moisture content (MC), oil color (OC), dielectric loss (Tan δ), and furans concentration specifically (2-furfuraldhyde (FA)). The statistical tools (correlation and multiple linear regression), based on 131 transformer samples, can be used to build a relation linking DP and one or more of the previous parameters to identify the insulating paper status and the percentage of remaining life of the transformer. The results indicated that it is difficult to build a mathematical model to relate between the DP and the testing variables, except with FA, where the trend of DP with FA is more obvious than with other variables. The empirical formula to compute DP based on the FA concentration was developed and gave promising results to compute DP and the remaining life of the power transformers. [ABSTRACT FROM AUTHOR]