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Nowadays, AC distributed power networks are facing many challenges in guaranteeing and improving the required level of power quality indices in power networks with increasing nonlinear, time-variable and unbalanced loads. Power networks can benefit from avoiding and minimizing different AC problems, such as frequency fluctuation and Total Harmonic Distortions (THDs), by using power filters, such as Hybrid Active Power Filters (HAPFs). Therefore, attention towards responsible power quality indices, such as Total Harmonic Distortion (THD), Power Factor (P.F) and Harmonic Pollution (HP) has increased. THD and HP are important indices to show the level of power quality at the network. In this paper, modern optimization techniques have been employed to optimize HAPF parameters, and minimize HP, by using a nature-inspired optimization algorithm, namely, Whale Optimization Algorithm (WOA). The WOA algorithm is compared to the most competitive powerful metaheuristic optimization algorithms: Manta Ray Foraging Optimization (MRFO), Artificial Ecosystem-based Optimization (AEO) and Golden Ratio Optimization Method (GROM). In addition, the WOA, and the proposed modern optimization algorithms, are compared to the most competitive metaheuristic optimization algorithm for HAPF from the literature, called L-SHADE. The comparison results show that the WOA algorithm outperformed all other optimization algorithms, in terms of minimizing harmonic pollution, through optimizing parameters of HAPF; therefore, this paper aims to present the WOA as a powerful control model for HAPF.

Recently, interest in DC networks and converters has increased due to the high number of applications in renewable energy systems. Consequently, the importance of improving the efficiency of the hybrid converters has been highlighted. An optimal control strategy is a significant solution to handle the challenges of controlling the hybrid interleaved boost–Cuk converter. In this article, a modern‐optimization‐methods‐based PWM strategy for a hybrid power converter is developed. In order to improve the performance of the hybrid converter, four modern optimization algorithms—namely, Manta ray foraging optimization (MRFO), Marine Predators Algorithm (MPA), Jellyfish Search Optimizer (JS), and Equilibrium Optimizer (EO)—are employed to minimize the input current ripple under different operation scenarios. The results of the proposed modern optimization algorithms have shown more efficient converter performance and balanced power‐sharing compared with conventional strategies and the literature on optimization algorithms such as Differential Evolution (DE) and Particle Swarm Optimization (PSO). In addition, the results of all operation cases presenting the proposed optimal strategy successfully reduced the input current ripple and improve the performance of power‐sharing at the converter compared with the conventional methods.

This paper proposes heterogeneous based ensemble Classifiers (voting and stacking method) to identify and classify different power system disturbances (power quality (PQ), faults, transients, and wind power variation) in wind integrated microgrid network. In the pre-processing stage of classification, a Discrete wavelet transform (DWT) technique is applied to extract the features from power system disturbance signals. The classification process for the proposed ensemble models involves two levels of classification. At the first level, the extracted features from the simulated power system events are used to learn the different individual base classifiers (logistic regression (LR), K-Nearest Neighbor (KNN), and J48 Decision tree (JDT)]. In second stage, a Meta-level classification is carried out based on predictions of base classifiers to get final predictions of class labels. First, the proposed ensemble models are utilized to discriminate the power system disturbances under random varying wind power condition and the predictive results (classification accuracy and performance indices) of ensemble models are compared with individual base classifiers (LR, KNN., and JDT). In addition, a sensitivity analysis is carried out under real time varying wind power condition and noisy environment of event signals to verify the effectiveness of ensemble models in further level. Furthermore, the robustness of proposed stacking ensemble model is verified with classification of single and combined PQ events of synthetic data, generated from the mathematical based PQ model software Predictions results under the different conditions show that stacking ensemble model offers substantial performance and discriminates the power disturbances with higher accuracy of classification than base classifiers and voting ensemble model.

Nanoparticles are generally anti-lipophilic and have a tendency to aggregate when they are embedded in nanofluids. Thus, surfactants have a major role in achieving long term dispersion stability of nanofluids through surface modification of nanoparticles. However, the surfactant changes the structure of the interfacial zone around nanoparticles and can have a crucial impact on the dielectric properties of dielectric nanofluids. Accordingly, this paper aims to clarify the role of interfacial zone on dielectric properties of vegetable oil based Al 2 O 3 nanofluids including relative permittivity, dissipation factor, and AC breakdown voltage. Moreover, a polarization model of nanofluids was proposed to calculate their relative permittivity considering surfactant effect. Different filler levels ranging from 0.01 wt% to 0.05 wt% were used and various temperature ranges were considered to validate the proposed model. Relative permittivity calculated from the proposed model was almost in line with experimental results. Proposed model depicted that surfactant contributes for orientational polarization of nanoparticles in the relative permittivity calculation. So, improvement in the relative permittivity of dielectric nanofluids has been attained from 0.01 wt% to 0.05 wt% of Al 2 O 3 nanoparticles due to both internal and orientational polarization of nanoparticles. Regarding dissipation factor of prepared nanofluids, it decreased against the weight percentage of nanoparticles, while the mean AC breakdown voltage increased against the weight percentage. All these effects were discussed considering the structure of interfacial zone and its impact on the alignment of oil chains and the energy loss of electrons.

This paper presents the detection of High Impedance Fault (HIF) in solar Photovoltaic (PV) integrated power system using recurrent neural network-based Long Short-Term Memory (LSTM) approach. For study this, an IEEE 13-bus system was modeled in MATLAB/Simulink environment to integrate 300 kW solar PV systems for analysis. Initially, the three-phase current signal during non-faulty (regular operation, capacitor switching, load switching, transformer inrush current) and faulty (HIF, symmetrical and unsymmetrical fault) conditions were used for extraction of features. The signal processing technique of Discrete Wavelet Transform with db4 mother wavelet was applied to extract each phase's energy value features for training and testing the classifiers. The proposed LSTM classifier gives the overall classification accuracy of 91.21% with a success rate of 92.42 % in identifying HIF in PV integrated power network. The prediction results obtained from the proffered method are compared with other well-known classifiers of K-Nearest neighbor's network, Support vector machine, J48 based decision tree, and Naïve Bayes approach. Further, the classifier's robustness is validated by evaluating the performance indices (PI) of kappa statistic, precision, recall, and F-measure. The results obtained reveal that the proposed LSTM network significantly outperforms all PI compared to other techniques.

This paper proposes a discrete wavelet transform (DWT)-based Graphical Language classifier algorithm for identification of high-impedance fault (HIF) in medium voltage (MV) distribution network of 13.8 kV. The proposed method of classifier is developed using virtual instrumentation LabVIEW facility, for detection of various faults such as symmetrical, unsymmetrical, and HIF in the system. Initially, the MV distribution feeder network has been modeled in MATLAB/Simulink, and the DWT analysis has been carried out with the introduction of various faults in the network to extract the features. The extracted features such as SD and energy values from the fault current signals have been applied to the proposed classifier algorithm to identify the type of fault. The effectiveness of the presented method has been tested and compared with the similar conventional fuzzy-based approach. The results indicate that the proposed classifier algorithm outperforms to give 100% accuracy, while the fuzzy-based approach misclassifies the double line to ground fault (LLG), three-phase fault (LLLG), and HIF. Furthermore, the proposed algorithm with LabVIEW facility is more flexible and can be implemented in real time using data acquisition unit for obtaining fault current signal from power system.

This paper proposes a new population-based hybrid particle swarm optimized-gravitational search algorithm (PSO-GSA) for tuning the parameters of the proportional-integral-derivative (PID) controller of a two-area interconnected dynamic power system with the presence of nonlinearities such as generator rate constraints (GRC) and governor dead-band (GDB). The tuning of controller parameters such as Kp, Ki, and Kd are obtained by minimizing the objective function formulated using the steady-state performance indices like Integral absolute error (IAE) of tie-line power and frequency deviation of interconnected system. To test the robustness of the propounded controller, the system is studied with system uncertainties, such as change in load demand, synchronizing power coefficient and inertia constant. The two-area interconnected power system (TAIPS) is modeled and simulated using Matlab/Simulink. The results exhibit that the steady-state and transient performance indices such as IAE, settling time, and control effort are impressively enhanced by an amount of 87.65%, 15.39%, and 91.17% in area-1 and 86.46%, 41.35%, and 91.04% in area-2, respectively, by the proposed method compared to other techniques presented. The minimum control effort of PSO-GSA-tuned PID controller depicts the robust performance of the controller compared to other non-meta-heuristic and meta-heuristic methods presented. The proffered method is also validated using the hardware-in-the-loop (HIL) real-time digital simulation to study the effectiveness of the controller.

My thesis focuses on quantification of the power quality issues in a solar photovoltaic based microgrid network through the simulation and experimental approach. PSO based optimization control strategy was also implemented to further improve the power quality factors in the Microgrid network

Microgrid (MG) is a small entity of electrical network which comprises of various Distributed Generation (DG) sources, storage devices, and group of loads in various classes. MG provides reliable and secure energy supply to the critical loads of communities while operating either in on-grid or off-grid mode. In this study, a coordinated power management control strategy for a typical low voltage (LV) MG network with integration of solar Photovoltaic (PV) and storage facility has been developed and analysed in Matlab-Simu- link software environment at various modes (on-grid, off-grid, and on-grid to off-grid transition) of MG operation. Solar PV and battery power inverters are considered as grid-support grid-forming (GsGfm) Voltage Source Inverter (VSI) with the implementation of modified droop and virtual output impedance control strategies. Proposed control strategy supports coordinated control operation between PV units and battery storage, equal power sharing among the DG sources, and smooth MG mode transition with regulation of voltage and frequency level in MG network. In addition, voltage and current THD level were analysed and verified as per the standard of AS4777.

Microgrid (MG) integrated with Distributed Generation (DG) provides several benefits like reliable, secure, and high efficient of energy supply, while minimizing power loss, deferring expansion of power distribution infrastructures, and reduced carbon emission of energy supply etc. to the communities. Despite of the several benefits, there are several challenges existing due to the integration of different characteristics and technology of DG sources in MG network. Power Quality (PQ) issue is one of the main technical challenges in MG power system. In order to provide improved PQ of energy supply, it is necessary to analyse and quantify the PQ level in MG network. This paper investigates the detail of PQ impacts in a real MG network carried out through an experimental analysis. Voltage and frequency variations/deviations are analysed in both on-grid and off-grid mode of MG operation at varying generation and varying load conditions. Similarly un-balance voltage and current level in neutral are estimated at unbalanced PV generation and uneven load distribution in MG network. Also current and voltage THD are estimated at different PV power level. Finally the results obtained from the analysis are compared to that of Australian network standard level.

Integration of solar PV and wind in to the distribution network is one of the most promising challenges of the modern power system networks to meet the growing demand of energy. Analysis of the effects of solar and wind intermittencies in the network are vital to maintain the power quality. Keeping this in view, this research paper focuses on impact analysis study of a typical power network with hybrid generation: solar PV and wind integration to quantify the level of impacts like power variation and voltage variation in the network through load flow analysis. Initially, a typical network model is developed using PSS-SINCAL and load profile analysis has been carried out based on the typical daily load profile and wind/solar profile to verify the power and voltage variations extensively in the network considering different scenarios. Results of this research analysis can be used as guidelines for utility grid to provide regulated and improved quality of energy supply by implementing appropriate planning of generation reserve and other control measures in the network

Microgrid (MG) power system plays an important role to fulfill reliable and secure energy supply to critical loads of communities as well as for communities in remote area. Distributed Generation (DG) sources integrated in a MG provides numerous benefits, at the same time leads to power quality issues in the MG power distribution network. Power Quality (PQ) issue arises due to the integration of an intermittent nature of Renewable Energy (RE) sources with advanced Power Electronics (PE) converter technology. Also, presence of non-linear and unbalancing loads in MG seems to affect PQ of the energy supply in power distribution network. In this paper, PQ impacts like; power variation, voltage variation, Total Harmonic Distortion (THD), and Unbalance voltage level have been analysed in Low Voltage (LV) distribution network of typical MG power system model. In this study, development of MG model and PQ impact analysis through simulation has been done in PSS-Sincal software environment. Analysis results from the study can be used as a guideline for developing a real and independent MG power system with improved PQ conditions.

The Australian Government has targeted 20% of electricity generation from renewable energy sources by 2020. The 20% renewable energy target (RET) for Australia will be around 45000GWH in 2020. Renewable energy of Victoria also includes as part of support to achieve 20% RET in Australia. In Victoria wind and solar resources are abundant as compared to other sources of renewable energy. Solar radiation and wind speed data for Victoria State was collected from NASA surface meteorology and solar energy web site. A feasibility analysis has been carried out to explore the potentialities of wind and solar energy for Victoria State using hybrid optimization model of electric renewable (HOMER) software. This paper also represents the estimation analysis of RET for Victoria and describes the integrating challenges of renewable energy sources to the utility grid.

Vinayagam, A., Aziz, A., Balasubramaniyam, P. M., Chandran, J., Veerasamy, V., & Gargoom, A. (2019). Harmonics assessment and mitigation in a photovoltaic integrated network. Sustainable Energy, Grids and Networks, 20, article 100264. https://doi.org/10.1016/j.segan.2019.100264