Change in Voltage Distortion Predictions at the PCC Due to Changing Nonlinear Load Current Profile Using Plant Startup Data

Customer loads connected to electricity supply systems may be broadly categorized as either linear or nonlinear. Nonlinear loads inject harmonics in a power distribution network. The interaction of the nonlinear load harmonics with the network impedances creates voltage distortions at the point of common coupling (PCC) which in turn affects other loads connected to the same PCC. When several nonlinear loads are connected to the PCC, it is difficult to predict mathematically how each nonlinear load is affecting the voltage distortion level at the PCC. Typically, customers with nonlinear loads apply harmonic filtering techniques to clean up their current and avoid penalties from the utility. When corrective action is taken by the customer, one important parameter of interest is the change in the voltage distortion level at the PCC due to the corrective action of the customer. This paper proposes a new method based on neural networks to predict the change in the distortion level of the voltage at the PCC if the customer were to draw only fundamental current and filter out its harmonics. The benefit of the proposed method is that it would indicate the impact of the customer\u27s front end filters on the voltage distortion at the PCC without actually having to install the filters. This paper presents the results of the proposed method applied to actual industrial sites

Neural Network Based Method for Predicting Nonlinear Load Harmonics

Generation of harmonics and the existence of waveform pollution in power system networks are important problems facing the power utilities. The increased use of nonlinear devices in industry has resulted in direct increase of harmonic distortion in the industrial power system in recent years. Interaction between loads and sources in a power distribution network is a complex process and often not possible to explain analytically without making assumptions. The determination of true harmonic current distortion of a load is further complicated by the fact that the supply voltage waveform at the point of common coupling (PCC) is rarely a pure sinusoid. This paper proposes a neural network based method to find a way of distinguishing between load contributed harmonics and supply harmonics, without disconnecting any load from the network. A neural network structure with memory is used to model the admittance of the nonlinear load. Once training is achieved, the neural network predicts the true harmonic current of the load if it could be supplied with a clean sine wave. The main advantage of this method is that only waveforms of voltages and currents have to be measured and is applicable for single phase as well as multiphase loads. This could be integrated into a commercially available power quality instrument or be fabricated as a standalone instrument that could be installed in substations of large customer loads, or used as a hand-held clip on instrument

Data-driven model-based approaches to condition monitoring and improving power output of wind turbines

The development of the wind farm has grown dramatically in worldwide over the past 20 years. In order to satisfy the reliability requirement of the power grid, the wind farm should generate sufficient active power to make the frequency stable. Consequently, many methods have been proposed to achieve optimizing wind farm active power dispatch strategy. In previous research, it assumed that each wind turbine has the same health condition in the wind farm, hence the power dispatch for healthy and sub-healthy wind turbines are treated equally. It will accelerate the sub-healthy wind turbines damage, which may leads to decrease generating efficiency and increases operating cost of the wind farm. Thus, a novel wind farm active power dispatch strategy considering the health condition of wind turbines and wind turbine health condition estimation method are the proposed. A modelbased CM approach for wind turbines based on the extreme learning machine (ELM) algorithm and analytic hierarchy process (AHP) are used to estimate health condition of the wind turbine. Essentially, the aim of the proposed method is to make the healthy wind turbines generate power as much as possible and reduce fatigue loads on the sub-healthy wind turbines. Compared with previous methods, the proposed methods is able to dramatically reduce the fatigue loads on subhealthy wind turbines under the condition of satisfying network operator active power demand and maximize the operation efficiency of those healthy turbines. Subsequently, shunt active power filters (SAPFs) are used to improve power quality of the grid by mitigating harmonics injected from nonlinear loads, which is further to increase the reliability of the wind turbine system