The distribution of measurement noise applied in practical power system state estimation (PSSE) can deviate from the assumed Gaussian model, and the performance of an estimator becomes bad if the Gaussian model is still used. This article proposes a new distributed robust PSSE method for multiarea power systems. The non-Gaussian model is utilized to fit the measurement noise distribution to reach high model accuracy. The proposed distributed method is derived based on the maximum correntropy criterion to further reduce the impact of non-Gaussian measurement noise and outliers. The influence function combined with the finite-time average consensus algorithm is used to implement the proposed distributed robust method in a fully distributed manner. Simulations conducted on the IEEE 30-bus, 118-bus and 300-bus systems and the Polish 2383-bus system demonstrate the robustness and effectiveness of the proposed distributed method. Each local area can get the system-wide robust state estimation solution by only using local information and small amounts of data from neighboring areas. Our proposed distributed robust method has at least 12% improvement in reducing the mean squared error under Gaussian-Uniform noise. It is verified the communication network applied for the proposed method is fairly flexible while the existing distributed approaches use a fixed communication network when the power systems are partitioned. The simulation results also verify the robustness of the proposed method to bad data and communication failure.

Distribution network accounts for a significant amount of real power loss in the power system. Minimization of losses in the network is desirable for economical and efficient operation of the system. One way of loss minimization is the optimal reconfiguration of the distribution network by selecting appropriate network switches to open. These open switches are termed as tie switches. As distribution network construction is closed loop, the open tie switches ensure that the radial nature of distribution network is maintained. The power loss can also be reduced by adding distributed generators (DGs) and shunt capacitors (SCs) locally near to the load points. This paper proposes an approach to simultaneously reconfigure the network, size and place both DGs and SCs in the network to minimize real power loss. LSHADE-EpSin algorithm is employed to perform the optimization task. Success history based parameter adaptation technique of differential evolution (DE) is termed as SHADE. LSHADE is the linear population size reduction technique of SHADE. LSHADE-EpSin introduces an additional adaptation technique for a control parameter during initial search stage to improve exploration capability. Standard IEEE-33 and IEEE-69 bus systems are tested with the algorithms. The results are found to be encouraging when compared with some of the recent studies.

Optimal power flow (OPF) is one of the highly non-linear, complex and challenging optimization problems in power system. The steady state parameters of an electrical network are determined in the process of solving OPF such that if recommended settings are followed, the operation of the network becomes economical and efficient. During earlier days, primary objective of OPF was minimization of fuel or generation cost. Due to growing importance on greenhouse gas emissions, power quality and system losses, numerous evolutionary algorithms (EAs) had been tried in the last couple of decades with several other objectives of OPF. This paper solves OPF with single objectives of minimizing fuel cost, emission and real power loss in the system. In addition, voltage stability enhancement is also set as an objective. Penalty function approach is adopted to deal with the system constraints which must be satisfied during the process of optimization. A state-of-the-art form of differential evolution (DE) algorithm, called L-SHADE, applied in solving the problem of OPF with different objectives. Success history-based parameter adaptation technique of DE is termed as SHADE. L-SHADE improves the performance of SHADE by linearly reducing the population size in successive generations. The algorithm is tested on standard IEEE 30-bus test system. Simulation results are analyzed and compared with some of the recent studies.

Multilevel inverters are mainly used for DC to AC power conversion and these inverters can be classified into types current source inverter (CSI) and voltage source inverter (VSI). Voltage source inverters are more common in power industry to convert lower levels of DC voltages into higher levels of AC voltages. In the process of conversion widely implemented pulse width modulated (PWM) switching technique of DC sources introduces harmonics in inverter output voltage. Total harmonic distortion (THD) is a measure of harmonic pollution in the power system and it is observed that variations in both DC voltages and switching angles of inverter affect the THD of inverter output voltage. Cascaded multilevel symmetric inverters ideally have DC sources all equal and constant. This paper considers inverters where DC sources can be unequal, a justifiable and realistic supposition. Optimal values of DC voltages and switching angles, which minimize THD level, are found using evolutionary algorithm. An advanced form of Differential Evolution (DE), called LSHADE-EpSin, is applied for the optimization problem. SHADE is a success history based parameter adaptation technique of DE. LSHADE improves the performance of SHADE with linearly reducing the population size in successive generations. LSHADE-EpSin introduces an additional adaptation technique for control parameters of the evolutionary algorithm. The algorithm has successfully been implemented for higher levels of inverters considered in the scope of our research study.

Setting of turbines in a windfarm is a complex task as several factors need to be taken into consideration. During recent years, researchers have applied various evolutionary algorithms to windfarm layout problem by converting it to a single objective and at the most two objective optimization problem. The prime factor governing placement of turbines is the wake effect attributed to the loss of kinetic energy by wind after it passes over a turbine. Downstream turbine inside the wake region generates less output power. Optimizing the wake loss helps extract more power out of the wind. The cost of turbine is tactically entwined with generated output to form single objective of cost per unit of output power e.g. cost/kW. This paper proposes an application of L-SHADE algorithm, an advanced form of Differential Evolution (DE) algorithm, to minimize the objective cost/kW. SHADE is a success history based parameter adaptation technique of DE. L-SHADE improves the performance of SHADE with linearly reducing the population size in successive generations. DE has historically been used mainly for optimization of continuous variables. The present study suggests an approach of using algorithm L-SHADE in discrete location optimization problem. Case studies of varying wind directions with constant and variable wind speeds have been performed and results are compared with some of the previous studies.

Short-term electricity load demand forecasting is a critical process in the management of modern power system. An ensemble method composed of Empirical Mode Decomposition (EMD) and Random Vector Functional Link network (RVFL) is presented in this paper. Due to the randomly generated weights between input and hidden layers and the close form solution for parameter tuning, RVFL network is a universal approximator with the advantages of fast training. By introducing ensemble approach via EMD into RVFL network, the performance can be significantly improved. Five electricity load demand datasets from Australian Energy Market Operator (AEMO) were used to evaluate the performance of the proposed method. The attractiveness of the proposed EMD based RVFL network can be demonstrated by the comparison with six benchmark methods.

A high efficiency hard switching constant current LED driver is presented with high overall efficiency, high current precision, high LED efficacy, flicker-free and wide constant current dimming ratio. The high stable lighting source provides the best solution for office light, reading light and LCD backlight.

Thin-film electronics in its myriad forms has underpinned much of the technological innovation in the fields of displays, sensors, and energy conversion over the past four decades. This technology also forms the basis of flexible electronics. Here we review the current status of flexible electronics and attempt to predict the future promise of these pervading technologies in healthcare, environmental monitoring, displays and human-machine interactivity, energy conversion, management and storage, and communication and wireless networks.

Two adaptive meshing schemes for the efficient simulation of dopant diffusion in silicon using the finite element method are presented. The first scheme is based on an adaptation criterion which is obtained by computing a local error estimate derived from the finite element solution itself. The second scheme is based on a mesh adaption criterion which uses physical diffusion parameters. A comparison is then made between the results obtained using the two different meshing schemes. Results obtained using both schemes are in very good agreement with each other; however, the remeshing scheme based on the physical criterion is easier to implement and reduces computation time by a factor of five as compared to the remeshing based on error evaluation. It is thus shown that a computationally efficient adaptive meshing strategy for dopant diffusion problems can be developed by employing a spatial discretization which limits the variation of the diffusion coefficient over an element.>