Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis

The useful planning and operation of the energy system requires a sustainability assessment of the system, in which the load model adopted is the most important factor in sustainability assessment. Having information about energy consumption patterns of the appliances allows consumers to manage their energy consumption efficiently. Non-intrusive load monitoring (NILM) is an effective tool to recognize power consumption patterns from the measured data in meters. In this paper, an unsupervised approach based on dimensionality reduction is applied to identify power consumption patterns of home electrical appliances. This approach can be utilized to classify household activities of daily life using data measured from home electrical smart meters. In the proposed method, the power consumption curves of the electrical appliances, as high-dimensional data, are mapped to a low-dimensional space by preserving the highest data variance via principal component analysis (PCA). In this paper, the reference energy disaggregation dataset (REDD) has been used to verify the proposed method. REDD is related to real-world measurements recorded at low-frequency. The presented results reveal the accuracy and efficiency of the proposed method in comparison to conventional procedures of NILM

Neural network-based modelling of wind/solar farm siting: a case study of East-Azerbaijan

The location of wind/solar power plants is a critical part of design process. Multi-criteria decision making (MCDM), the well-known procedure of site selection, suffers from the local-scoring property. This paper proposes a combined approach of MCDM and artificial neural networks (ANN) to alleviate this deficiency. Here, the weighting of site selection criteria has been performed using the analytic hierarchy process (AHP), and then a multi-layer perceptron (MLP) is used for implementing the global scoring capability. By using this procedure, adding any new alternative site location cannot affect the scores of the others. In other words, the proposed procedure is global-scale and robust. Scores derived by this procedure for two candidate sites can be interpreted as real differences in these sites

Locating Inter-Turn Faults in Transformer Windings Using Isometric Feature Mapping of Frequency Response Traces

Power transformers usually confront various mechanical and electromagnetic stresses during an operation that may lead to defects in their windings. The short circuit in the windings is one of those severe defects. Early detection of short-circuits is necessary as extra heating in the shorted location can lead to progressive damage in windings insulation. Frequency response analysis (FRA) is a well-known method to diagnose short-circuits in transformers. Despite the accuracy of FRA, the interpretation of the obtained frequency response traces (FRTs) is still an intricate task. Due to the unknown impact of faults on FRTs, extracting efficient features from such traces is necessary for the interpretation of transformer\u27s frequency response. In this article, an isometric feature mapping (Isomap) is used as a nonlinear dimensionality reduction technique to locate interturn faults in transformer windings due to its capability of capturing the nonlinear phenomena in FRT of power transformers. It is revealed that, after constructing the isometric mapping for a transformer, there is no need for any expertise to detect fault location even in nondirect (high impedance) short-circuits. In other words, it can be the first step for the automated interpretation of FRA of power transformers

Transition in Iran’s Electricity Market Considering the Policies on Elimination of Electricity Subsidies: System Dynamics Application

Because of electricity subsidies, electricity price in Iran is much lower than its real value, and the growth of electricity demand is much more than its rational rate, which in turn implies ever increasing investment in the electricity section by the Government. Therefore, the recent Government policies are based on elimination of electricity subsidies, followed by commissioning complete electricity market to attract investors in the power industry. In this paper, a model is developed for electricity demand prediction and evaluating Iran's current electricity market and complete market to deal with optimistic and pessimistic electricity demand. Hence, a system dynamics framework is applied to model and generate scenarios because of its physical capability and information flows that allow understanding the of behavior nonlinear dynamics in uncertain conditions. To validate the model, it was compared with the available actual data within 21 years, since (1988-2008). After model validation, two scenarios are evaluated based on the influence of eliminating electricity subsidies on electricity demand in short-term and long-term and then commissioning of the probable complete electricity market is evaluated. For this purpose, first, the electricity demand is estimated for the target years and then changing dynamics in transition of Iran’s electricity market is analyzed

Hybrid CNN-LSTM approaches for identification of type and locations of transmission line faults

Timely and accurate detection of transmission line faults is one of the most important issues in the reliability of the power systems. In this paper, in order to assess the effects of impedance and location of the fault in identifying and classifying it, the frequency response analysis (FRA) method is utilized. This method clearly shows the smallest effects of the faults on voltage and current signals in the frequency domain. Interpretation of the results associated with the FRA procedure is considered a weakness of this method. To overcome this issue and accurately categorize types and locations of various transmission lines faults such as asymmetric faults and symmetric faults, machine learning, and deep learning applications called support vector machine (SVM), decision tree (DT), k-Nearest Neighbors (k-NN), convolutional neural network (CNN), long short term memory (LSTM), and a hybrid model of convolutional LSTM (C-LSTM) are utilized. Introduced faults are applied with various impedances in 6 segments of an IEEE standard transmission line system. Then, the frequency response curves (FRCs) for them are computed and selected as input datasets for the suggested networks. After categorizing the types and locations of faults, the results for each network are analyzed via different statistical performance evaluation metrics. Finally, in order to early detection of faults, the new high impedance faults (7000 and 9000 O) are applied based on the previous routine in the transmission line. At this stage, evaluations demonstrate the capability of the C-LSTM followed by SVM, DT, k-NN, CNN, and LSTM in categorizing the type and location of transmission line faults.</p