Enhanced decoupling current scheme with selective harmonic elimination pulse width modulation for cascaded multilevel inverter based static synchronous compensator

This dissertation is dedicated to a comprehensive study and performance analysis of the transformer-less Multilevel Cascaded H-bridge Inverter (MCHI) based STATic synchronous COMpensator (STATCOM). Among the shunt-connected Flexible AC Transmission System (FACTS) controllers, STATCOM has shown extensive feasibility and effectiveness in solving a wide range of power quality problems. By referring to the literature reviews, MCHI with separated DC capacitors is certainly the most versatile power inverter topology for STATCOM applications. However, due to the ill-defined transfer functions, complex control schemes and formulations were emerged to achieve a low-switching frequency high-bandwidth power control. As a result, adequate controller parameters were generally obtained by using trial and error method, which were practically ineffective and time-consuming. In this dissertation, the STATCOM is controlled to provide reactive power (VAR) compensation at the Point of Common Coupling (PCC) under different loading conditions. The goal of this work is to enhance the performance of the STATCOM with the associated proposed control scheme in achieving high dynamic response, improving transient performance, and producing high-quality output voltage waveform. To evaluate the superiority of the proposed control scheme, intensive simulation studies and numerous experiments are conducted accordingly, where a very good match between the simulation results and the experimental results is achieved in all cases and documented in this dissertation

Cascaded multilevel inverter based STATCOM with power factor correction feature

The paper investigates the STATCOM system based on five-level cascaded inverter controlled using carrier-based pulse width modulation (CB-PWM) technique. In this work, the STATCOM is controlled to provide only the reactive power compensation at the point of common coupling (PCC) when an RL load is connected to the power system at different lagging power factors. The rotating switching scheme is employed for the DC link voltage balancing while the phase angle control scheme is used in the STATCOM main system. A new current reference control method is proposed in this work and the effectiveness and the theoretical prediction of the proposed approach for different loading conditions is validated through simulation studies using MATLAB SIMULINK software package

Multi-level Signal Decomposition for Power Quality Disturbance Classification

The introduction of electric vehicles impose large disturbance to the grid-level power signal due to the charging and discharging mechanism. Power signal monitoring in the electrical grid can provide several insights such as power quality disturbance detection, major power consumption area, peak power usage period, and their potential catastrophic failure conditions. As for preventive maintenance purpose, automatic classification of power quality disturbance using a hybrid method incorporating wavelet transform and deep LSTM network is proposed in this paper. Multi-level signal decomposition is applied to input signal to increase the resolution of input decomposing into multiple frequency bands. Subsequently, these multi-level frequency components are fed into deep LSTM layer to further extract useful higher order latent feature. Classification performance of the proposed wavelet-based LSTM (WTLSTM) network is bench-marked with deep LSTM method. Additive white Gaussian noise (AWGN) with signal-to-noise (SNR) levels between 20-50dB are inserted during the training process to increase the generalization of signal learning with the realistic scenarios. The classification performance of both WT-LSTM and Deep LSTM networks are tested with 20,30,40,50dB SNR AWGN and noiseless conditions. As a result, the WT-LSTM network obtains an overall classification performance of 89.77% on 20dB and 99.21% on noiseless condition as compared to Deep LSTM, with 88.48% and 98.54% respectively

Optimisation of neural network with simultaneous feature selection and network prunning using evolutionary algorithm

Most advances on the Evolutionary Algorithm optimisation of Neural Network are on recurrent neural network using the NEAT optimisation method. For feed forward network, most of the optimisation are merely on the Weights and the bias selection which is generally known as conventional Neuroevolution. In this research work, a simultaneous feature reduction, network pruning and weight/biases selection is presented using fitness function design which penalizes selection of large feature sets. The fitness function also considers feature and the neuron reduction in the hidden layer. The results were demonstrated using two sets of data sets which are the cancer datasets and Thyroid datasets. Results showed backpropagation gradient descent error weights/biased optimisations performed slightly better at classification of the two datasets with lower misclassification rate and error. However, features and hidden neurons were reduced with the simultaneous feature /neurons switching using Genetic Algorithm. The number of features were reduced from 21 to 4 (Thyroid dataset) and 9 to 3 (cancer dataset) with only 1 hidden neuron in the processing layer for both network structures for the respective datasets. This research work will present the chromosome representation and the fitness function design

Optimisation of Neural Network with Simultaneous Feature Selection and Network Prunning using Evolutionary Algorithm

Most advances on the Evolutionary Algorithm optimisation of Neural Network are on recurrent neural network using the NEAT optimisation method. For feed forward network, most of the optimisation are merely on the Weights and the bias selection which is generally known as conventional Neuroevolution. In this research work, a simultaneous feature reduction, network pruning and weight/biases selection is presented using fitness function design which penalizes selection of large feature sets. The fitness function also considers feature and the neuron reduction  in the hidden layer. The results were demonstrated using two sets of data sets which are the cancer datasets and Thyroid datasets. Results showed backpropagation gradient descent error weights/biased optimisations performed slightly better at classification of the two datasets with lower misclassification rate and error. However, features and hidden neurons were reduced with the simultaneous feature /neurons switching using Genetic Algorithm. The number of features were reduced from 21 to 4 (Thyroid dataset) and 9 to 3 (cancer dataset) with only 1 hidden neuron in the processing layer for both network structures for the respective datasets.  This research work will present the chromosome representation and the fitness function design

The smart IOT earth leakage circuit breaker with transformerless and SMPS auto recloser

Earth leakage Circuit Breaker (ELCB) is function as a protection device that shall install either in industrial or residential protect electrical appliance or user from electrical shock or current leakage. When ELCB fault is detected, mechanical switch trigger the system to trip the circuit breaker. The problem faced by the user are the need to access to distribution box (DB) when tripping occur to turn it on manually. Some home appliance such as computer and alarm system should always connected to supply to prevent data from crashing or system mulfunction. The Smart Internet of Thing (IOT) Earth Leakage Circuit Breaker with Transformerless and SMPS Auto Recloser is proposed in this project to solve the need of always standby near to the DB to turn on after trip. This project has been improved from previous study with some transformation of circuit and prototype design. Transformerless circuit develop on this project eliminated the need of bulky transformer which usually used on linear circuit to power the microcontroller for control system to automatically recloser ELCB. Microcontroller used on this project are ESP32 module link with the IoT module to access from another location within the range. The Switch Mode Power Supply (SMPS) circuit is design to power DC motor in order to recloser the ELCB. The transformerless and SMPS is designed in two separate circuit to prevent microcontroller from damage while starting the DC motor. The auto recloser is set for 15 time tripping to prevent continuously fault that protect wiring and appliance from danger

Loss minimization DTC electric motor drive system based on adaptive ANN strategy

Electric motor drive systems (EMDS) have been recognized as one of the most promising motor systems recently due to their low energy consumption and reduced emissions. With only some exceptions, EMDS are the main source for the provision of mechanical energy in industry and accounts for about 60% of global industrial electricity consumption. Large energy efficiency potentials have been identified in EMDS with very short payback time and high-cost effectiveness. Typical, during operation at rated mode, the motor drive able to hold its good efficiencies. However, a motor usually operates out from rated mode in many applications, especially while under light load, it reduced the motor’s efficiency severely. Hence, it is necessary that a conventional drive system to embed with loss minimization strategy to optimize the drive system efficiency over all operation range. Conventionally, the flux value is keeping constantly over the range of operation, where it should be highlighted that for any operating point, the losses could be minimize with the proper adjustment of the flux level to a suitable value at that point. Hence, with the intention to generate an adaptive flux level corresponding to any operating point, especially at light load condition, an online learning Artificial Neural Network (ANN) controller was proposed in this study, to minimize the system losses. The entire proposed strategic drive system would be verified under the MATLAB/Simulink software environment. It is expected that with the proposed online learning Artificial Neural Network controller efficiency optimization algorithm can achieve better energy saving compared with traditional blended strategies

Loss minimization DTC electric motor drive system based on adaptive ANN strategy

Electric motor drive systems (EMDS) have been recognized as one of the most promising motor systems recently due to their low energy consumption and reduced emissions. With only some exceptions, EMDS are the main source for the provision of mechanical energy in industry and accounts for about 60% of global industrial electricity consumption. Large energy efficiency potentials have been identified in EMDS with very short payback time and high-cost effectiveness. Typical, during operation at rated mode, the motor drive able to hold its good efficiencies. However, a motor usually operates out from rated mode in many applications, especially while under light load, it reduced the motor’s efficiency severely. Hence, it is necessary that a conventional drive system to embed with loss minimization strategy to optimize the drive system efficiency over all operation range. Conventionally, the flux value is keeping constantly over the range of operation, where it should be highlighted that for any operating point, the losses could be minimize with the proper adjustment of the flux level to a suitable value at that point. Hence, with the intention to generate an adaptive flux level corresponding to any operating point, especially at light load condition, an online learning Artificial Neural Network (ANN) controller was proposed in this study, to minimize the system losses. The entire proposed strategic drive system would be verified under the MATLAB/Simulink software environment. It is expected that with the proposed online learning Artificial Neural Network controller efficiency optimization algorithm can achieve better energy saving compared with traditional blended strategie

Loss minimization DTC electric motor drive system based on adaptive ANN strategy

Electric motor drive systems (EMDS) have been recognized as one of the most promising motor systems recently due to their low energy consumption and reduced emissions. With only some exceptions, EMDS are the main source for the provision of mechanical energy in industry and accounts for about 60% of global industrial electricity consumption. Large energy efficiency potentials have been identified in EMDS with very short payback time and high-cost effectiveness. Typical, during operation at rated mode, the motor drive able to hold its good efficiencies. However, a motor usually operates out from rated mode in many applications, especially while under light load, it reduced the motor’s efficiency severely. Hence, it is necessary that a conventional drive system to embed with loss minimization strategy to optimize the drive system efficiency over all operation range. Conventionally, the flux value is keeping constantly over the range of operation, where it should be highlighted that for any operating point, the losses could be minimize with the proper adjustment of the flux level to a suitable value at that point. Hence, with the intention to generate an adaptive flux level corresponding to any operating point, especially at light load condition, an online learning Artificial Neural Network (ANN) controller was proposed in this study, to minimize the system losses. The entire proposed strategic drive system would be verified under the MATLAB/Simulink software environment. It is expected that with the proposed online learning Artificial Neural Network controller efficiency optimization algorithm can achieve better energy saving compared with traditional blended strategie

Enhanced decoupling current scheme with selective harmonic elimination pulse width modulation for cascaded multilevel inverter based static synchronous compensator

This dissertation is dedicated to a comprehensive study and performance analysis of the transformer-less Multilevel Cascaded H-bridge Inverter (MCHI) based STATic synchronous COMpensator (STATCOM). Among the shunt-connected Flexible AC Transmission System (FACTS) controllers, STATCOM has shown extensive feasibility and effectiveness in solving a wide range of power quality problems. By referring to the literature reviews, MCHI with separated DC capacitors is certainly the most versatile power inverter topology for STATCOM applications. However, due to the ill-defined transfer functions, complex control schemes and formulations were emerged to achieve a low-switching frequency high-bandwidth power control. As a result, adequate controller parameters were generally obtained by using trial and error method, which were practically ineffective and time-consuming. In this dissertation, the STATCOM is controlled to provide reactive power (VAR) compensation at the Point of Common Coupling (PCC) under different loading conditions. The goal of this work is to enhance the performance of the STATCOM with the associated proposed control scheme in achieving high dynamic response, improving transient performance, and producing high-quality output voltage waveform. To evaluate the superiority of the proposed control scheme, intensive simulation studies and numerous experiments are conducted accordingly, where a very good match between the simulation results and the experimental results is achieved in all cases and documented in this dissertation