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A new hybrid self-adaptive training approach-based radial basis function (RBF) neural network for power transformer fault diagnosis is presented in this paper. The proposed method is able to generate RBF neural network models based on fuzzy c-means (FCM) and quantum-inspired particle swarm optimization (QPSO), which can automatically configure network structure and obtain model parameters. With these methods, the number of neuron, centers and radii of hidden layer activated functions, as well as output connection weights can be automatically calculated. This learning method is proved to be effective by applying the RBF neural network in the classification of five benchmark testing data sets, and power transformer fault data set. The results clearly demonstrated the improved classification accuracy compared with other alternatives and showed that it can be used as a reliable tool for power transformer fault analysis. [ABSTRACT FROM AUTHOR]

Deregulation of the electric power industry has introduced new uncertainties for market participants and made planning of transmission expansion more difficult. More flexible transmission expansion plans are needed, to cope with the increased risks. In this paper, a novel planning approach is proposed to meet the above challenge. In our approach, the planning process is modeled as a mixed integer nonlinear programming (MINLP) problem, so that conflicting objectives can be optimized simultaneously. To minimize planning risks, our method identifies several scenarios based on statistics and expert knowledge; the most flexible expansion plan is selected as the plan that has least adaptation cost. The proposed method is tested with the IEEE 14-bus system. Promising results are obtained to demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

The application of evolutionary computation methods in search and optimization has been growing over the past few decades. As a promising approach in metaheuristic optimization algorithms, differential evolution (DE) has been attracting increasing attention for wide engineering applications including power engineering. Different from conventional evolutionary algorithms using predefined probability distribution function for mutation process, differential evolution exploits the differences of randomly sampled pairs of objective vectors for its mutation process. Consequently the variation between vectors will outfit the objective functions topographical information toward the optimization process, and therefore provides efficient global optimization capability. However, although DE is shown to be precise, fast as well as robust, its search efficiency will be impaired during solution process with fast descending diversity of population. In this paper, detailed numerical studies are carried out to propose the characterization of the performance of several DE mutation methods with and without fitness sharing scheme. All the approaches using the proposed modified DE are presented on an example in power system planning. [ABSTRACT FROM AUTHOR]

Electricity price forecasting is a difficult yet essential task for market participants in a deregulated electricity market. Rather than forecasting the value, market participants are sometimes more interested in forecasting the prediction interval of the electricity price. Forecasting the prediction interval is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions. In this paper, a novel data mining-based approach is proposed to achieve two major objectives: 1) to accurately forecast the value of the electricity price series, which is widely accepted as a nonlinear time series; 2) to accurately estimate the prediction interval of the electricity price series. In the proposed approach, support vector machine (SVM) is employed to forecast the value of the price. To forecast the prediction interval, we construct a statistical model by introducing a heteroscedastic variance equation for the SVM. Maximum likelihood estimation (MLE) is used to estimate model parameters. Results from the case studies on real-world price data prove that the proposed method is highly effective compared with existing methods such as GARCH models. [ABSTRACT FROM AUTHOR]

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining-based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are first described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms—support vector machine and probability classifier—are chosen to be the spike occurrence predictors and are discussed in detail. Realistic market data are used to test the proposed model with promising results. [ABSTRACT FROM AUTHOR]

The BR algorithm is a novel and efficient method to find all eigenvalues of upper Hessenberg matrices and has never been applied to eigenanalysis for power system small signal stability. This paper analyzes differences between the BR and the QR algorithms with performance comparison in terms of CPU time based on stopping criteria and storage requirement. The BR algorithm utilizes accelerating strategies to improve its performance when computing eigenvalues of narrowly banded, nearly tridiagonal upper Hessenberg matrices. These strategies significantly reduce the computation time at a reasonable level of precision. Compared with the QR algorithm, the BR algorithm requires fewer iteration steps and less storage space without depriving of appropriate precision in solving eigenvalue problems of large-scale power systems. Numerical examples demonstrate the efficiency of the BR algorithm in pursuing eigenanalysis tasks of 39-, 68-, 115-, 300-, and 600-bus systems. Experiment results suggest that the BR algorithm is a more efficient algorithm for large-scale power system small signal stability eigenanalysis. [ABSTRACT FROM AUTHOR]

The reconstruction of power industries has brought fundamental changes to both power system operation and planning. This paper presents a new planning method using a multi-objective optimization (MOOP) technique, as well as human knowledge, to expand the transmission network in open-access schemes. The method starts with a candidate pool of feasible expansion plans. Consequent selection of the best candidates is carried out through a MOOP approach, of which multiple objectives are tackled simultaneously, aiming at integrating the market operation and planning as one unified process in context of a deregulated system. Human knowledge has been applied in both stages to ensure the selection with practical engineering and management concerns. The expansion plan from MOOP is assessed by reliability criteria before it is finalized. The proposed method has been tested with the IEEE 14-bus system, and relevant analyses and discussions have been presented. [ABSTRACT FROM AUTHOR]

Power flow feasibility boundaries are constraints limiting the operating transmission capability and the available stability (security) margin of the system. The convexity properties of this boundary are important to the analytical description of its shape (including the needs of approximation) and for the calculation of security margins. These analyses are significantly simplified if the boundary is convex. The conjecture that the boundary is convex in the space of system parameters has been used over a long time. However, some alarming experimental counterexamples already exist which show that this boundary is not always convex. In this letter, an analytical approach is presented to analyze the convexity properties of the power flow feasibility boundary. In particular, the approach helps to identify nonconvex parts of the boundary. Numerical case studies illustrate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Emission trading is widely considered to be the most effective policy to minimize the overall costs for CO2 abatement. However, the political feasibility of an emission trading scheme may crucially depend on the free initial allocation of emission permits to carbon-intensive industries in order to offset the reduction in profits.This paper aims to analyze these potential profit impacts and the possible compensation to affected generation compaflies through modeling the Australian National Electricity Market under a potential emission trading scheme. Historical emissionbased and historical generation-based allocation approaches are used in this paper to calculate and compare the percentages of carbon permits that should be freely allocated. Two carbon permit price scenarios are -used to analyze the sensitivity of the optimal percentage of free allocation to carbon permit price. [ABSTRACT FROM AUTHOR]

Load model is one of the most important elements in power system simulation and control. Based on more than 20 years of practice using load characteristic recorders in the measurement-based load modeling, this paper proposes the Expectation Composite Load Model to predict unseen data. The methods for load bus classification and parameter identification are also provided. The generalization capability of the proposed load modeling is validated by the measured load dynamics and system dynamics during two three-phase short circuit tests of the NE power grid in China. In order to evaluate the measurement-based load model, the Probabilistic Collocation Method (PCM) is applied to quantitatively analyze uncertainties of the simulation responses raised by the parameters. [ABSTRACT FROM AUTHOR]

A load model is one of the most important elements in power system simulation and control. Recently, the constant impedance, constant current, and constant power load in combination with the induction motor load have been widely used as the composite load model, whose parameters are all identified from the field measurements in measurement-based load modeling practices so far. However, there is virtually no research conducted on whether all these parameters could really be identified. This paper investigates the possibility on reducing the number of composite load model parameters to be identified from field measurements. This paper first shows that direct application of the IEEE load motor parameters in the composite load model may be inadequate on describing the load dynamics over different operating status. Then the perturbation method is used to derive the trajectory sensitivities of the equivalent motor parameters, based on which the reduction on the identified parameters of the composite load model is presented. Two cases of measurement-based load modeling in North China and Northeast China are studied to illustrate the validity of the reduction method. It is shown that the reduction does not lose the model's capability on describing the load dynamics. The reduction on the number of identified parameters not only provides a possible way to solve the multi-valued load model problem based on the current practices on measurement-based load modeling, but it also facilitates building of the load model with more components included in it. Meanwhile, reducing the identified parameters reduces the identification time; thus, the proposed strategy significantly enhances the efficiency of the load modeling work. [ABSTRACT FROM AUTHOR]

Research effort has been given to locate the optimal locations of thyristor-controlled series capacitor (TCSC) and their initial compensation levels using mixed-integer programming (MIP). As a useful technique for combinatorial optimisation over integer and continuous variables, the MIP approach can provide robust performance as well as high computational efficiency while solving complex optimal problems. Previous work using MIP employed dc load flow model ignoring reactive power balance, power loss and transformer tap ratios. In this paper, a new planning method is developed based on recently reported line flow equations and basic linearisation of binary-continuous products. The objectives of the planning strategy are to improve system loadability, voltage profile in the network, as well as to minimise the investment cost by choosing proper locations and settings of devices. Simulation results are presented and discussed for IEEE 9-, 57-, 118-, and 300-bus systems. [ABSTRACT FROM AUTHOR]