Decision tree-based approach for online management of pem fuel cells for residential application

This thesis demonstrates a new intelligent technique for the online optimal management of PEM fuel cells units for on site energy production to supply residential utilizations. Classical optimization techniques were based on offline calculations and cannot provide the necessary computational speed for online performance. In this research, a Decision Tree (DT) algorithm was employed to obtain the optimal, or quasi-optimal, settings of the fuel cell online and in a general framework. The main idea was to employ a classification technique, trained on a sufficient subset of data, to produce an estimate of the optimal setting without repeating the optimization process. A database was extracted from a previously�performed Genetic Algorithm (GA)-based optimization that has been used to create a suitable decision tree, which was intended for generalizing the optimization results. The approach provides the flexibility of adjusting the settings of the fuel cell online according to the observed variations in the tariffs and load demands. Results at different operating conditions are presented to confirm the high accuracy of the proposed generalization technique. The accuracy of the decision tree has been tested by evaluating the relative error with respect to the optimized values. Then, the possibility of pruning the tree has been investigated in order to simplify its structure without affecting the accuracy of the results. In addition, the accuracy of the DTs to approximate the optimal performance of the fuel cell is compared to that of the Artificial Neural Networks (ANNs) used for the same purpose. The results show that the DTs can somewhat outperform the ANNs with certain pruning levels

Small-Signal Stability Analysis of The Hydrokinetic Energy Harnessing connected to The Grid

This paper presents the modelling of the hydrokinetic system for the small-signal stability analysis under the small disturbances due to variation and fluctuation of water velocity in the river or marine. The complete modelling of the hydrokinetic system consists of vertical axis H-Darrieus turbine, direct-drive permanent magnet synchronous generator (PMSG), back-to-back converter and the grid network. By linearising all the equation around the steady-state value, the dynamic equation of the hydrokinetic system is derived. The stability of the system is tested with and without the PI controller. The eigenvalues analysis-based approaches have been used to investigate the stability of the system under the small disturbances. The findings show, the stability of the hydrokinetic system with PI controller is improved up to 57.82% by reducing the oscillation frequency at the Rotor Side Converter (RSC)

Kalman Filter Estimation of Impedance Parameters for Medium Transmission Line

Accurate knowledge of impedance parameters in transmission line helps to improve the system efficiency and performance. Nowadays, the estimation of impedance parameters in transmission line has become possible with the availability of computational method. This paper aims to develop Kalman filter model by using Matlab simulink to estimate accurate values of resistance (R), reactance (X), and susceptance (B) in medium transmission line. The accuracy of the parameters can be improved by reducing the unknown errors in the system. To demonstrate the effectiveness of the Kalman filter method, a case study of simulated medium transmission line is presented and comparison between Kalman Filter (KF) and Linear Least Square (LLS) method is also considered to evaluate their performances

Hybrid HCS-fuzzy mppt algorithm for hydrokinetic energy harnessing

This paper proposes a hybrid maximum power point tracking (MPPT) algorithm for the stand-alone hydrokinetic technology in river application. The fixed stepsize Hill Climbing Search (HCS) algorithm is widely used due to its simplicity. However, the operating point oscillates around the maximum power point (MPP) during dynamic steady-state resulting waste some amount of energy. The proposed algorithm is the combination of the Fuzzy Logic Controller (FLC) and HCS algorithm to provide the variable step-size to eradicate the limitation of conventional HCS algorithm. The proposed algorithm has been compared with the Small Step HCS (SS-HCS) to investigate the performance of the algorithm. The simulation results illustrated the proposed algorithm (Fuzzy-HCS) improved the output power with 89.91 % efficiency and minimizes oscillation during dynamic steady-state

Managing productivity in Universiti Malaysia Pahang: Rethinking the whom, which, what, and whose of productivity

Drawing on reviews of scholarly literature, this study suggests rethinking productivity in Universiti Malaysis Pahang (UMP) along four dimensions: the productivity of whom, productivity for which unit of analysis, productivity according to what functions, and productivity in whose interests. It offers principles for promoting enlightened discussion and pursuit of productivity of academic staff at UMP. In contrast to the dominant discussion, which emphasises focus, centralised standard measures, and accountability, the bias unfairness in this study is toward balance, decentralised diversity, and recalibration. Academic Differentiated Career Pathways (ADCAP) suggest the ideal is not for academic staff and faculties to produce to centrally managed objectives but for all individuals and units faculties to manage individually and collectively to design their work to improve their productivity along multiple dimensions

Assessment of energy storage and renewable energy sources-based two-area microgrid system using optimized fractional order controllers

The stability of modern power systems faces significant challenges due to intermittent renewable generation and fluctuating load demands, resulting in excessive frequency variations. To address this issue and prevent potential blackouts, implementing a robust control strategy is crucial. This study introduces a cascaded control system, combining a novel fractional order integral derivative (FOID) with a fractional order proportional integral derivative with filter (FOPIDN) controllers, denoted as CFOID-FOPIDN. The gain parameters for CFOID-FOPIDN are determined using the Archimedes optimization algorithm (AOA). The proposed two-area microgrid system incorporates various power-generating sources, including solar, wind turbine generators, fuel cells, micro-turbines, and battery energy storage technologies. To assess the AOA-tuned CFOID-FOPIDN controller's effectiveness, the system is tested under five different conditions: load fluctuations, changes in wind speed, variations in irradiance, combined wind and irradiance variations, and comprehensive changes across all conditions. Simulation results reveal that the AOA-based CFOID-FOPIDN outperforms other existing algorithms, such as particle swarm optimization (PSO), bat algorithm (BAT), moth flame optimization (MFO), and whale optimization algorithm (WOA). In dynamic variations across all sources, the AOA-tuned strategy demonstrates shorter settling times in area-1 (2.5 s), area-2 (2.2 s), and tie-line power (2.1 s) compared to PSO (3.5 s, 3.8 s, 3.4 s), WOA (3.2 s, 3.4 s, 3.1 s), BA (3.0 s, 3.1 s, 3.0 s), and MFO (2.9 s, 2.8 s, 2.9 s). Across all five scenarios, the AOA-tuned CFOID-FOPIDN strategy consistently demonstrates superior performance compared to the other strategies under consideration

Optimized controllers for stabilizing the frequency changes in hybrid wind-photovoltaic-wave energy-based maritime microgrid systems

Reducing the dependence on traditional energy sources and shifting towards the utilization of renewable energy sources (RES) of energy in the maritime sector is imperative for reducing greenhouse gas emissions. Inherently, RES sources like solar and wind are intermittent and variable, resulting in inconsistent power availability and hence leading to energy supply fluctuations and potential shortages. In this respect, an efficient control strategy to maintain system stability and address intermittency effectively is essential. This work considers a hybrid marine microgrid with various energy sources like photovoltaics (PV), wind energy conversion system (WECS), marine biodiesel generator, Archimedes wave power generation, solid oxide fuel cell, and batteries. A 2-degree of freedom (2DOF) structure is designed and implemented with the tilt-integral-derivative filter (TIDN) to address frequency variations. Furthermore, an Archimedes optimization algorithm (AOA) is used to optimize the 2DOF-TIDN controller. The stability of the proposed microgrid system is assessed under various combinations of RES availabilities, including real-time data from WECS and PV. The AOA-based 2DOF-TIDN performance is compared to the following algorithms: genetic, Jaya, bat, grasshopper optimization, particle swarm optimization, and moth flame optimization. Simulation results obtained show that the AOA-based 2DOF-TIDN control strategy achieves shorter settling times in mitigating the changes of marine microgrid systems under different dynamic conditions as compared to the other algorithms. Finally, the controller being proposed in this paper was tested for robustness with parameter deviations of +25%, −20%, and − 40% from the nominal values, and proved to be the proposed 2DOF-TIDN controller parameters demonstrate significant robustness in effectively managing the uncertainties and parametric variations

The potential of hydrokinetic energy harnessing in pahang river basin

This paper focuses on studying the potential of the hydrokinetic energy harnessing along the longest river in Peninsular Malaysia, which is the Pahang River. The data such water discharge and water depth on ten selected sites at the Telemetry Gauging Station (GS) owned by Department of Drainage and Irrigation, Malaysia (DID) have been used for the assessment of hydrokinetic potentials. The Flow Duration Curve (FDC) at the potential site has been plotted to analyse the Q50. This assessment study indicated that the two rivers along the Pahang River basin have a significant potential for hydrokinetic energy harnessing. Subsequently, four different types of turbines with different size and power coefficient (Cp) has been used to calculate the output power and total annual energy yield. The estimated annual energy yield for Sg. Pahang at Lubuk Paku is ranging from 69.5 to 173.7 MWh. Whereas Sg. Pahang at Temerloh is between 45.54 and 113.8 MWh per year

A novel fault-detection methodology of proposed reduced switch MLI fed induction motor drive using discrete wavelet transforms

Induction motors are typically promoted in industrial applications by adopting energy-efficient power-electronic drive technology. Multilevel inverters (MLI) have been widely recognized in recent days for high-power, medium-voltage-efficient drives. There has been vital interest in forming novel multilevel inverters with reduced switching elements. The newly proposed reduced-switch five-level inverter topology extends with fewer switches, low dv/dt stress, high efficiency, and so on, over the formal multilevel inverter topologies. The multilevel inverter's reputation is greatly affected due to several faults on switching elements and complex switching sequences. In this paper, a novel fault identification process is evaluated in both healthy and faulty conditions using discrete-wavelet transform analysis. The discrete wavelet transform utilizes the multi-resolution analysis with a feature extraction methodology acquired for fault identification over the classical methods. A novel fault identification scheme is implemented on reduced-switch five-level MLI topology using the Matlab/Simulink platform to increase the drive system's reliability. The effectiveness of simulation outcomes is illustrated with proper comparisons. The pro posed topology's hardware model is implemented using a dSPACE DS1103 real-time digital controller and the results of the experiment are presented

Optimal power flow with stochastic solar power using barnacles mating optimizer

This work proposes the implementation of recent evolutionary metaheuristic algorithm namely, barnacles mating optimizer (BMO) to solve the Optimal Power Flow (OPF) issue. BMO is inspired by the mating behaviour of barnacles which happened in two ways: by normal copulation and sperm-cast. The effectiveness of the proposed BMO in solving the OPF is tested on a modified IEEE-30 bus system that is integrated with solar PV farms for five cases viz (1) cost minimization of the power generation that consists of thermal and stochastic solar power generations, (2) power loss minimization, (3) voltage deviation minimization, (4) emission minimization and (5) combined cost and emission minimization of power generations. To demonstrate the effectiveness and the veracity of the solution obtained by BMO, several recent algorithms that have been reported in the literature will be utilized and compared intensively. In the end, the simulation results demonstrate that the BMO can be effectively becoming an alternative solution for the OPF issue in general