• Energy Research

The present study focuses on the selection of appropriate wavelet basis which would result in better classification of Power Quality (PQ) events. The accuracy of the classification is often critically dependent on the nature of wavelet basis and the induction algorithm. This study, therefore, comprehensively investigates the performance of several wavelet families including Daubechies, Coiflets, Symlets, Fejer-Korovkin, Bi-orthogonal and Reverse Bi-orthogonal from the prospective of PQ event identification. The performance of these wavelets was evaluated through fourteen distinct single and simultaneous PQ events which were generated following IEEE Std. 1159. Further, to investigate the interaction between induction algorithm and wavelet basis, two induction algorithms were included: k-Nearest Neighbor (k-NN) and Naive Bayes (NB). The results of the investigation convincingly demonstrate that ‘db18’ from the Daubechies family provides the best overall performance among 110 wavelet bases included in this study.

A Variable-Speed Wind Turbine (VSWT) can serve as a good reservoir of Kinetic Energy (KE) for few seconds owing to wide operating rotor speed. Based on this fact, several approaches have been proposed to introduce synthetic inertial response in VSWT. Usually, this is accomplished by introducing an additional control loop at the outer most level of the control hierarchy. However, several key issues including the selection of control parameters and the effects of wind speed variations on the synthetic inertial support are not addressed. As a result, the KE reserve is severely under utilized. To address these concerns, in this work, a simple approach is proposed to control parameter selection which ensures the optimal use of available KE reserve. Further, to tackle the variable KE reserve, a comprehensive inertia controller using intelligent learning paradigm is designed. The proposed inertia controller can adapt to wind speed variations while providing optimum inertial response. Efficacy of the proposed approach is evaluated over the entire operating range of the VSWT. For further evaluation, wind speed data from NREL western wind integration is utilized. The results indicate that the proposed system is quite effective and can maintain an adequate performance over the entire operating range.

The use of kinetic energy, stored in the rotational masses of Variable Speed Wind Turbines (VSWT), for inertial support is well established. The idea is to employ the fast control response of the VSWT to inject additional power for the short duration following the disturbance. However, the variable speed operation of the VSWT poses a great challenge in successfully designing an appropriate control approach, applicable for wide operating ranges, capable of minimizing the effects of energy regain by the Wind Turbine (WT) after the support period. To address this issue, this paper proposes a modified inertia-emulation scheme, based on Step Over-Production (SOP) approach. Further, to enable optimum energy transfer and to handle the problem of variable Stored Kinetic Energy (SKE), the shaping parameters of the proposed scheme are optimized using the Particle Swarm Optimization (PSO) algorithm. The results show that the proposed approach can limit the fall of frequency while reducing post disturbances across the entire operating range of the WT. In addition, the quantitative analysis reveals that the proposed method can easily satisfy the stringent grid code requirements for the inertia emulation and provides a better alternative to the conventional inertia control architectures.

The present study proposes an alternate approach to design and shape the Emulated Inertial Response (EIR) from Variable Speed Wind Turbines (VSWT) by exchanging its kinetic energy (KE) with the grid. Although, the KE released by the deceleration of VSWT can momentarily reduce the generation-demand difference, and thereby alleviate the frequency stability, limiting the effects of subsequent VSWT recuperation is one of the major challenge of EIR. The selection of the parameters of inertia controller which can balance the see-saw effects of KE exchange is a difficult task due to non-linear nature of the system. Further, the EIR control parameters must be selected judiciously to satisfy multiple and conflicting power system objectives. To address these issues, the present study uses the notion of Pareto-dominance along with Multi-Objective Particle Swarm Optimization (MOPSO) to select EIR control parameters. The efficacy of the proposed approach has been illustrated via simulations considering a case study on a micro-grid. The simulation results demonstrate that the designed EIR could effectively shape the inertial response as per decision makers requirement.

A novel method of classifying Power quality (PQ) events using Wavelet Packet Transform (WPT) and Extreme Learning Machines (ELM) has been proposed. In recent times, the power quality has been a major research concern due to changing regulations, liberalized distribution market and increased use of power electronic based equipment. The first step of any remedial action requires proper identification of PQ events. One of the major challenge of this event identification is to extract significant features from the limited measurements, which can subsequently be used for the classification. Therefore, in the present study Wavelet Packet Transform (WPT) has been used to obtain several mathematical features. These features can segregate both single and simultaneous PQ event occurrences. Further to improve the classification performance, the ELM based classifier has been used. This classifier significantly reduces the training time by many-fold. The performance of the proposed approach has been compared with ANN based classifier considering over 1000 PQ signals from various PQ events. The results of the simulation demonstrate that the proposed approach can achieve over 99% classification accuracy.

This paper presents a robust control framework with meta-heuristic intelligence to optimize the energy performance of air handling units (AHUs) and to maximize the thermal comfort of occupants by judiciously selecting the temperature set points of two controllers (i.e., the H∞ controller and the boiler controller). The selection of these set points is formulated as a multi-objective optimization problem, where the goal is to balance energy consumption with thermal comfort. Furthermore, the uncertainty weights of the H∞ controller are estimated to minimize oscillations in the outflow air temperature of the AHU plant. The performance of the proposed framework is investigated by considering the real-time weather data of Auckland, New Zealand. The results of the simulation show that the proposed robust control framework could significantly reduce oscillations in the outflow air temperature compared with the conventional case, where the temperature set points are selected empirically. Moreover, annual energy savings of 49.13% are achieved without compromising the thermal comfort.

Abstract The present study proposes a novel methodology based on a meta-heuristic algorithm to accurately estimate the thermal parameters of a building. These parameters are often most difficult to be quantified in practice as these are strictly determined by the characteristics of the materials used in building construction, which vary significantly from one building structure/type to another. The parameter estimation problem is formulated as a single-objective optimization problem and the thermal model of the building has been developed in EnergyPlusTM, following black-box identification strategy. Performance of the proposed approach is demonstrated considering both practical and simulated energy consumption data of the building, after subjecting the thermal model to two different geographical environments of New Zealand, with significant weather profile variations. The results of the investigation illustrate that the proposed methodology could effectively estimate the building thermal parameters. This approach, therefore, will facilitate in developing efficient Building Energy Management Systems (BEMS).

This study comprehensively investigates power quality (PQ) identification problem and proposes the optimum combination of base wavelet and machine learning algorithm (MLA) which would yield the highest classification accuracy. Although this problem has been studied by various researchers in the recent past, the selection of appropriate base wavelet and MLA, which would give better classification accuracy, have received comparatively less attention. This study bridges this gap by investigating the classification performance of 110 wavelets and 7 well‐known MLAs across various noise levels using over 3500 PQ events generated as per IEEE Standard 1159. The results of this investigation demonstrate that the choice of base wavelet does significantly affect the classification performance. Further, it was observed that a single base wavelet does not provide optimum performance across all MLAs at various noise levels. In contrast, each MLA gives the maximum accuracy with a distinct base wavelet. The robustness of MLA against noise is studied which establishes that the simple MLAs, such as decision tree and Naive‐Bayes, are more robust against noise compared to the other intricate MLAs. Finally, several recommendations are drawn for the selection of base wavelet and MLA which yields the best possible accuracy.

Light Emitting Diode (LED) streetlights are playing a critical role in Smart City initiatives of various governments across the world as they facilitate better control over energy usage with a comparatively smaller carbon footprint. The increasing share of LED technology in the global street-lighting market can be considered as one of the major breakthroughs in the street-lighting industry. It is, therefore, necessary to assess this technology from various perspectives. In this study, we evaluate the performance of streetlight from various manufacturers in the presence of grid disturbances. For this purpose, three distinct grid disturbances have been considered, i.e., voltage sag, swell and harmonics. The results indicate that high level of current harmonics could be expected during voltage swell and sag events. Further, the presence of source harmonics has a distinct influence on the streetlights from different manufacturers owing to different converter topologies.