New Classifier Design for Static Security Evaluation Using Artificial In-telligence Techniques

This paper proposes evaluation and classification classifier for static security evaluation (SSE) and classifica-tion. Data are generated on (30, 57, 118 and 300) bus IEEE test systems used to design the classifiers. The implementation decision tree methods on several IEEE test systems involved appropriateness SSE and classi-fication by using four algorithms of DT’s. Empirically, with the present of FSA, the implementation results indicate that these classifiers have the capability for system security evaluation and classification. Lastly, FSA is efficient and effective approach for real-time evaluation and classification classifier design

Static security classification and evaluation classifier design in electric power grid with presence of PV power plants using C-4.5

Energy suppliers all over the world must expand energy in a way that is secure, clean, affordable, and environmentally responsible. Photovoltaic (PV) has been a competitive renewable-energy source for the power generation mix in the world. With the presence of solar PV technology, this paper proposes C4.5 approach for static security evaluation and classification (SSE). This paper proposes PV generators connected to the grid when bilateral energy transactions with the loads are implemented to see their impacts on the system security. To build a classifier in binary class, the process is divided into four components: data collection, pre-processing and feature selection, comparison of the techniques, best classifier selection and performance evaluation. A comprehensive comparison of four of Decision Tree׳s Algorithms for SSE is conducted. The study is (accomplished using) conducted on IEEE 30 bus system, which comprises 5 PV power generators deliver a total power of 40 MW. Data are generated on (30, 57, 118 and 300) bus IEEE test systems used to train and test the classifiers. Empirically, with the presence of PV power generators, the implementation results indicate that these classifiers have the capability for system security evaluation and classification. Lastly, C4.5 is an efficient and effective approach for real-time evaluation and classification classifier desig

Identification of Acoustic Signals of Internal Electric Discharges on Glass Insulator under Variable Applied Voltage

A Partial Discharge (PD) is an unwanted phenomenon in electrical equipment. Therefore it is of great importance to identify different types of PD and assess their severity. This paper investigates the acoustic emissions associated with Internal Discharge (ID) from different types of sources in the time-domain. An experimental setup was arranged in the high voltage laboratory, a chamber with an electrode configuration attached to it was connected to a high voltage transformer for generating various types of PD. A laboratory experiment was done by making the models of these discharges. The test equipment including antennas as a means of detection and digital processing techniques for signal analysis were used. Wavelet signal processing was used to recover the internal discharge acoustic signal by eliminating the noises of many natures

Decision tree for static security assessment classification

This paper addresses the on going work of the application of Machine Learning on Static Security Assessment of Power Systems. Several techniques, which have been applied for static Security Assessment .A Decision Tree types comparison for the purpose of static security assessment classification is discussed and the comparison results from these methods on operating point are presented. Decision Tree examines whether the power system is secured under steadystate operating conditions.DT gauges the bus voltages and the line flow conditions. Using minimum number of cases from the available large number of contingencies in terms of their impact on the system security is the methodology that has been developed. Newton Raphson load flow analysis method is used for training and test data. The input variables to the network are loadings of the lines and the voltage magnitude of the load buses. The algorithms are initially tested on the 5 IEEE bus systems. The results obtained indicate that DT method is comparable in accuracy and computational time to the Newton Raphson load flow method

Machine learning for steady state security assessment in power system

The objective of this paper is to investigate the reliability of the SSA in determining the security level of power system from serious interference during operation. Artificial Neural Network (ANN), Adaptive Network Based Fuzzy Inference System (ANFIS) and Decision Trees (DT) are implemented to classify the security status in the test power system, comparison are made in terms of computation time and accuracy of the networks. Impact of Attribute Selections on train and test set is proposed. The impact of attributes number and cross validation on performance of the train and test data set is proposed as well. Data obtained from Newton Raphson Load Flow (NRLF) analysis method are used for the training and testing purposes of the proposed AI techniques. The data are used also as a benchmark to validate the results from AI techniques to achieve high speed of execution and good classification accuracy. A new methodology of feature selection technique based on extracting variables has also been applied. The proposed techniques have been extended and tested on various IEEE test systems. Generally, the proposed AI techniques have successfully been applied to evaluate SSA for various IEEE test system

New classifier design for static security evaluation using artificial intelligence techniques

This paper proposes evaluation and classification classifier for static security evaluation (SSE) and classifica-tion. Data are generated on (30, 57, 118 and 300) bus IEEE test systems used to design the classifiers. The implementation decision tree methods on several IEEE test systems involved appropriateness SSE and classification by using four algorithms of DT's. Empirically, with the present of FSA, the implementation results indicate that these classifiers have the capability for system security evaluation and classification. Lastly, FSA is efficient and effective approach for real-time evaluation and classification classifier design

Artificial neural network-based photovoltaic module temperature estimation for tropical climate of Malaysia and its impact on photovoltaic system energy yield

This article presents an artificial neural network (ANN)-based approach for predicting photovoltaic (PV) module temperature using meteorological variables. The proposed approach utilizes actual hourly records of various meteorological parameters, such as ambient temperature Ta, solar irradiation G, relative humidity RH, and wind speed Ws as input variables. The hourly meteorological data were collected over 9?months in the year 2009 from a 92-kWp installed PV system in Selangor, Malaysia. The data were divided into two sets: training data, which are a set of 1849 (April–October) hourly data, and 578 (November–December) hourly records of working as test data. Four ANN models have been developed by using different combination of meteorological parameters as inputs, and, for each model, the output is the PV module temperature Tm. It was found that the model using all parameters, including RH and Ws as inputs, gave the most accurate results with correlation coefficient (r) 95.9%, and 0.41, 0.1, and 4.5% for MBE, RMSE, and MPE, respectively. To show the superiority and applicability of the developed ANN model, results from the proposed ANN model have been compared with the conventional model adopted by Malaysia Energy Center and another mathematical model based on regression. With the model's simplicity, the proposed approach can be used as an effective tool for predicting the PV module temperature, for any type of PV systems, in remote or rural locations with no direct measurement equipments. The developed model also will be very useful in studying PV system performance and estimating its energy output

Evaluation of theoretical and experimental mass transfer limitation in steam reforming of phenol-PET waste to hydrogen production over Ni/La-promoted Al2O3 catalyst

Theoretical and experimental approaches were achieved to estimate the presence of mass transfer limitations using Ni/La-co-promoted Al2O3 catalyst for selective reaction from polyethylene terephthalate (PET) plastic waste via steam reforming process. The catalysts were prepared by impregnation method and were characterized using, BET, TPD-CO2, TPR-H2, SEM-EDX, TGA and DTA. The internal and external diffusions were indicated in the experimental technique, by changing the catalyst pellet size (dp) and feed flow rate (ml/min). Dimensionless parameters in the theoretical method such as Thiele modules (φ1), effectiveness factor (η), and overall effectiveness factor (Ω) were evaluated. From the experimental results, due to the decreasing of PET-Phenol conversion (from 89 to 76%) at different pellet size, internal mass transfer limitation was indicated. In addition, the theoretical approaches of internal mass transfer limitation was observed in PET-Phenol reaction for all catalyst pellet sizes due to effectiveness factor less than 1 (η ≠ 1). This is shown that experimental results are in complete agreement with the theoretical approach. The presence of external and internal diffusion for all catalytic pellet sizes also confirmed from the overall effectiveness factor evaluation (Ω)

Renewable hydrogen production from bio-oil derivative via catalytic steam reforming: An overview

Tremendous research efforts have been dedicated towards development and utilization of sustainable alternative energy resources. Depletion of fossil fuels and the rising environmental concerns such as global warming are among the reasons that necessitated such. Hydrogen (H2) has been widely considered a clean fuel for the future, with the highest mass based energy density among known fuels. Bio-oil components are the most renewable energy carriers produced from bio-mass which have been selected for hydrogen production. Phenol and acetic acid are among the major liquid waste components of the bio oil. Catalytic steam reforming of these components in a fixed bed reactor provides a promising technique for hydrogen production from renewable sources. Due to the vital interaction that exists between catalyst and supports, Rh and Ni active metals and ZrO2, La2O3 and CeO2 supports were found to be appropriate catalysts with long-term stability for the hydrogen production via steam reforming of phenol and acetic acid. The process is advantageous due to its high hydrocarbon conversion and H2/CO2 product ratio. The present work provides extensive information about the phenol and acetic acid steam reforming process for producing hydrogen as a renewal energy carrier