Set-membership filtering for systems with sensor saturation

This paper addresses the set-membership filtering problem for a class of discrete time-varying systems with sensor saturation in the presence of unknown-but-bounded process and measurement noises. A sufficient condition for the existence of set-membership filter is derived. A convex optimisation method is proposed to determine a state estimation ellipsoid that is a set of states compatible with sensor saturation and unknown-but-bounded process and measurement noises. A recursive algorithm is developed for computing the ellipsoid that guarantees to contain the true state by solving a time-varying linear matrix inequality. Simulation results are provided to demonstrate the effectiveness of the proposed method

Preliminary statistical study of low voltage distribution feeders under a representative network in Western Australia

This paper presents a preliminary clustering analysis study on a set of LV feeders connected to a selected prototypical HV feeder that represents the light suburban residential class in Western Australian. The study led to the identification of several subsets of LV feeders where each represents specific types of users. The intended use of the LV feeder cluster data is to develop representative test case LVfeeders. These can be applied in the impact assessment of technology change including Smart Grid deployments or load changes due to solar panel, air conditioning or electric vehicleuptake. The work will be expanded to include LV feeder clusteranalysis for networks supplied from other HV feeder types

Set-membership fuzzy filtering for nonlinear discrete-time systems

This paper is concerned with the set-membership filtering (SMF) problem for discrete-time nonlinear systems. We employ the Takagi–Sugeno (T-S) fuzzy model to approximate the nonlinear systems over the true value of state and to overcome the difficulty with the linearization over a state estimate set rather than a state estimate point in the set-membership framework. Based on the T-S fuzzy model, we develop a new nonlinear SMF estimation method by using the fuzzy modeling approach and the S-procedure technique to determine a state estimation ellipsoid that is a set of states compatible with the measurements, the unknown-but-bounded process and measurement noises, and the modeling approximation errors. A recursive algorithm is derived for computing the ellipsoid that guarantees to contain the true state. A smallest possible estimate set is recursively computed by solving the semidefinite programming problem. An illustrative example shows the effectiveness of the proposed method for a class of discrete-time nonlinear systems via fuzzy switch

A statistical study on topological features of high voltage distribution networks in Western Australia

The assessment of changes in technology or load behaviors on distribution networks requires a rigorous understanding of the features of the network itself. In a typical distribution network, there will be hundreds of high voltage (HV) feeders and ten thousands of low voltage (LV) feeders. This work uses cluster analysis to identify statistically representative or prototypical HV feeders in the West Australian context. The representative HV feeder data will be used as an input to the development of Monte Carlo models to assess the impact of technology changes driven by Smart Grid deployments or load changes due to solar panel, air conditioning or electric vehicleuptake

Robust set-membership filtering for systems with missing measurement : a linear matrix inequality approach

This study addresses the robust set-membership finite-horizon filtering problem for a class of discrete time-varying systems with missing measurement and polytopic uncertainties in the presence of unknown-but-bounded process and measurement noises. A robust set-membership filter is developed and a recursive algorithm is derived for computing the state estimate ellipsoid that is guaranteed to contain the true state. An optimal possible estimate set is computed recursively by solving the semi-definite programming problem. Simulation results are provided to demonstrate the effectiveness of the proposed method

Statistical identification of prototypical low voltage distribution feeders in Western Australia

This paper presents a statistical clustering analysis study for low voltage (LV) distribution feeders in Western Australian (WA), Australia. The work identified a group of prototypical LV feeders from thousands of feeders, where each feeder represents specific types of users and electrical loads. The prototypical feeder data is used to identify physical feeders that are a set of representative test cases for LV feeders in the WA context. The test case feeders can be used as statistically meaningful representative cases for the assessment of technology impacts including smart grid deployments or load changes due to photovoltaics or electric vehicle uptake

A hybrid model for residential loads in a distribution system with high PV penetration

This paper presents a hybrid model for residential electricity demand. Consumer load is modeled in two parts. A low frequency model uses a compact Fourier series to represent slowly changing diurnal loads. Cluster analysis is applied to identify particular load classes and a load classifier based upon quadratic discriminant functions is developed. Once the low frequency load components are removed a high frequency residual load remains. This component is modeled using a power spectrum representation. The load modeling approach was used to model consumption on high demand days for households within the Perth Solar City high photovoltaic penetration feeder trials. The load models were successfully validated against physical data sets with respect to load aggregation behaviors and load cumulative probability functions

Statistical discriminant analysis of high voltage feeders in Western Australia distribution networks

The distribution network impact assessment of changes in load behaviors or new technology deployments requires a rigorous understanding of the features of the network itself. In a typical distribution network, there will be hundreds of high voltage (HV) feeders and ten thousands of low voltage (LV) feeders. This paper presents a method to combine cluster and discriminant analysis techniques to identify a small number of statistically representative or prototypical feeders that capture the key features of a distribution network. The proposed method is readily transferable to other systems. As an illustration the paper presents a representative HV feeder set for an existing network and a feeder classifier based uponquadratic discriminant functions. Representative feeder datawill be typically used as an input to Monte Carlo models toassess the impact of technology changes driven by Smart Griddeployments or load changes due to distributed renewables, airconditioning or electric vehicle uptake

Taxonomic description for western Australian distribution medium-voltage and low-voltage feeders

A typical distribution network contains hundreds of medium-voltage (MV) feeders and ten thousands of low-voltage (LV) feeders. This work introduces an efficient taxonomy approach that combines cluster analysis with discriminant analysis to identify statistically representative MV and LV feeders in the west Australian context. Quadratic discriminant functions have been extracted and can be used as a feeder classifier for any feeder in this distribution system. The representative feeder sets provide rigorously validated test cases for the evaluation of smart grid technologies

Exploring the factors inducing contractors’ unethical behavior: Case of China

The construction industry is experiencing more serious ethical problems than ever before. The objectives of this study are to (1) identify the inducers of individuals’ unethical behavior in contractors’ organizations in the Chinese construction industry; and (2) investigate the interrelationships among these inducers. Based on a literature review and interviews, 18 factors inducing unethical behavior were identified, and a questionnaire survey was performed, which garnered 129 responses. The results showed that 13 inducers were significantly important, and that “cost pressures,” “inadequate sanctions,” and “absence of ethics systems” were the top three inducers of unethical behavior. Additionally, the 18 inducers were categorized into 5 underlying groupings: culture-related inducers; policy environment; project pressures; individual traits; and organizational climate. Further analysis results indicated that culture-related inducers indirectly influence organizational climate through their direct impact on policy environment, project pressures, and individual traits. The proposed framework describing the inducers and the intergrouping relationships provides an understanding of the formation mechanism of contractors’ unethical behavior