A Novel Hybrid GWO-LS Estimator for Harmonic Estimation Problem in Time Varying Noisy Environment

The power quality of the Electrical Power System (EPS) is greatly affected by electrical harmonics. Hence, accurate and proper estimation of electrical harmonics is essential to design appropriate filters for mitigation of harmonics and their associated effects on the power quality of EPS. This paper presents a novel statistical (Least Square) and meta-heuristic (Grey wolf optimizer) based hybrid technique for accurate detection and estimation of electrical harmonics with minimum computational time. The non-linear part (phase and frequency) of harmonics is estimated using GWO, while the linear part (amplitude) is estimated using the LS method. Furthermore, harmonics having transients are also estimated using proposed harmonic estimators. The effectiveness of the proposed harmonic estimator is evaluated using various case studies. Comparing the proposed approach with other harmonic estimation techniques demonstrates that it has a minimum mean square error with less complexity and better computational efficiency

Signal Processing and Soft Computing Approaches to Power Signal Frequency and Harmonics Estimation

Frequency and Harmonics are two important parameters for power system control and protection, power system relaying, power quality monitoring, operation and control of electrical equipments. Some existing approaches of frequency and harmonics estimation are Fast Fourier Transform (FFT), Least Square (LS), Least Mean Square (LMS), Recursive Least Square (RLS), Kalman Filtering (KF), Soft Computing Techniques such as Neural Networks and Genetic Algorithms etc. FFT based technique suffers from leakage effect i.e. an effect in the frequency analysis of finite length signals and the performance is highly degraded while estimating inter-harmonics and sub-harmonics including frequency deviations. Recursive estimation is not possible in case of LS. LMS provides poor estimation performance owing to its poor convergence rate as the adaptation step-size is fixed. In case of RLS and KF, suitable initial choice of covariance matrix and gain leading to faster convergence on Mean Square Error is difficult. Initial choice of Weight vector and learning parameter affect the convergence characteristic of neural estimator. Genetic based algorithms takes more time for convergence

A hybrid recursive least square pso based algorithm for harmonic estimation

The presence of harmonics shapes the performance of a power system. Hence harmonic estimation of paramount importance while considering a power system network. Harmonics is an important parameter for power system control and enhance power system relaying, power quality monitoring, operation and control of electrical equipments. The increase in nonlinear load and time varying device causes periodic distortion of voltage and current waveforms which is not desirable electrical network. Due to this nonlinear load or device, the voltage and current waveform contains sinusoidal component other than the fundamental frequency which is known as the harmonics. Some existing techniques of harmonics estimation are Least Square (LS), Least Mean Square (LMS),Recursive Least Square (RLS), Kalman Filtering (KF), Soft Computing Techniques such as Artificial neural networks (ANN),Least square algorithm, Recursive least square algorithm, Genetic algorithm(GA) ,Particle swarm optimization(PSO) ,Ant colony optimization, Bacterial foraging optimization(BFO), Gravitational search algorithm, Cooker search algorithm ,Water drop algorithm, Bat algorithm etc. Though LMS algorithm has low computational complexity and good tracking ability ,but it provides poor estimation performance due to its poor convergence rate as the adaptation step-size is fixed. In case of RLS suitable initial choice of covariance matrix and gain leading to faster convergence. The thesis also proposed a hybrid recurvive least square pso based algorithm for power system harmonics estimation. In this thesis, the proposed hybrid approaches topower system harmonics estimation first optimize the unknown parametersof the regressor of the input power system signal using Particle swarm optimization and then RLS are applied for achieving faster convergence in estimating harmonics of distorted signal

Hybrid Weighted Least Square Multi-Verse Optimizer (WLS–MVO) Framework for Real-Time Estimation of Harmonics in Non-Linear Loads

Data Availability Statement: Not applicable.Copyright © 2023 by the authors. The electric power quality has become a serious concern for electric utilities and end users owing to its undesirable effects on system capabilities and performance. Harmonic levels on power systems have been pronounced to a greater extent with the continuous growth in the application of solid-state and reactive power compensatory devices. Harmonics are the key constituents that are mainly responsible for power quality deterioration. Power system harmonics need to be correctly estimated and filtered to increase power quality. This research work focuses on accurate estimation of power system harmonics with the proposed hybrid weighted least-square multi-verse optimizer (WLS–MVO) based framework. Multi-verse optimizer replicates the phenomenon of the formation of new universes as described by multi-verse theory to solve complex real-world optimization problems. The proposed WLS–MVO framework is tested and validated by estimating the harmonics present in multiple test signals with different noise levels. Amplitudes and phases of harmonics present in the polluted signal were estimated, and the framework computational time was compared with the previously developed technique’s results which are reported in the literature. There was 80% reduction in computational time and 82% improvement in terms of accuracy in estimating harmonics using WLS–MVO as compared to previously developed techniques. The performance of the developed framework is further validated by estimating the harmonics present in the real-time voltage and current waveforms obtained from axial flux permanent magnet generator (AFPMSG), uninterruptible power supply (UPS), and light-emitting diode (LED). The purposed technique technique outperforms the already-developed techniques, in terms of accuracy and computational time.This research received no external funding

Application of Power Electronics Converters in Smart Grids and Renewable Energy Systems

This book focuses on the applications of Power Electronics Converters in smart grids and renewable energy systems. The topics covered include methods to CO2 emission control, schemes for electric vehicle charging, reliable renewable energy forecasting methods, and various power electronics converters. The converters include the quasi neutral point clamped inverter, MPPT algorithms, the bidirectional DC-DC converter, and the push–pull converter with a fuzzy logic controller

Application of Spectral Kurtosis to Characterize Amplitude Variability in Power Systems’ Harmonics

The highly-changing concept of Power Quality (PQ) needs to be continuously reformulated due to the new schemas of the power grid or Smart Grid (SG). In general, the spectral content is characterized by their averaged or extreme values. However, new PQ events may consist of large variations in amplitude that occur in a short time or small variations in amplitude that take place continuously. Thus, the former second-order techniques are not suitable to monitor the dynamics of the power spectrum. In this work, a strategy based on Spectral Kurtosis (SK) is introduced to detect frequency components with a constant amplitude trend, which accounts for amplitude values’ dispersion related to the mean value of that spectral component. SK has been proven to measure frequency components that follow a constant amplitude trend. Two practical real-life cases have been considered: electric current time-series from an arc furnace and the power grid voltage supply. Both cases confirm that the more concentrated the amplitude values are around the mean value, the lower the SK values are. All this confirms SK as an effective tool for evaluating frequency components with a constant amplitude trend, being able to provide information beyond maximum variation around the mean value and giving a progressive index of value dispersion around the mean amplitude value, for each frequency component

Non-intrusive efficiency estimation of inverter-fed induction machines

Motorised loads using induction machines use approximately 60% of the electricity globally. Most of these systems use three-phase induction motors due to their robustness and lower cost. They are often installed in continuously operating industrial plants/applications that require no operational interruptions. Whilst most of these induction machines are supplied from ideally sinusoidal supplies, applications are emerging where induction machines are fed from non-sinusoidal supplies. In particular, pulse width modulated inverters realize efficient control of induction machines in many automated industrial applications. From an energy management perspective, it is vital to continually assess the efficiency of induction machines in order to initiate replacement or economic repair. It is therefore of paramount importance that reliable and non-intrusive techniques for efficiency estimation of induction machines be investigated, that consider sinusoidal and non-sinusoidal supplies. This work proposes a non-intrusive efficiency estimation technique for inverter–fed induction motors that is based on harmonic regression analysis, harmonic equivalent circuit parameter estimation and harmonic loss analysis using limited measured data. Firstly, considerations for inverter-fed induction motor equivalent circuit modelling and parameter estimation techniques suitable for non-intrusive efficiency estimation are presented and the selection of one equivalent circuit for analysis is justified. Measured data is obtained from two different induction motors on a flexible 110kW test rig that utilises an HBM Gen 7i data acquisition system. By measuring voltage, current and input power at the supply terminals of the inverter-fed motor, the fundamental equivalent circuit parameters are estimated using population based incremental learning algorithm and compared with those obtained from the IEC 60034-2-1 Standard. The harmonic parameters are estimated using the bacterial foraging algorithm basing on the input impedance of the motor at each harmonic order. A finite harmonic loss analysis is carried out on the tested induction motors. The proposed techniques and harmonic loss analysis provide accurate efficiency estimates of within 1.5% error when compared to the direct method. Lastly, a related non-intrusive efficiency estimation technique is proposed that caters for a holistic loss contribution by all harmonics. The efficiency results from the proposed techniques are compared to those obtained from the IEC-TS 60034-2-3 Technical Specification and a direct method. The estimated efficiencies are comparable to those measured by the Technical Specification and a direct method within 2% error when tested on 37kW and 45kW PWM inverter-fed motors across the loading range. Furthermore, this work conducts a comprehensive non-intrusive rotor speed estimation comparative analysis in order to recommend the best technique(s), in terms of intrusiveness, accuracy and computational overhead. Errors of less than 1% have been reported in literature and experimental verification when using vibration analysis, Motor Current Signature Analysis (MCSA), Rotor Slot Harmonic (RSH) and Rotor Eccentricity Harmonic (REH) analysis techniques in inverter-fed IMs

Voltage Management Of Distribution Networks With High Penetration Of Distributed Photovoltaic Generation Sources

Installation of photovoltaic (PV) units could lead to great challenges to the existing electrical systems. Issues such as voltage rise, protection coordination, islanding detection, harmonics, increased or changed short-circuit levels, etc., need to be carefully addressed before we can see a wide adoption of this environmentally friendly technology. Voltage rise or overvoltage issues are of particular importance to be addressed for deploying more PV systems to distribution networks. This dissertation proposes a comprehensive solution to deal with the voltage violations in distribution networks, from controlling PV power outputs and electricity consumption of smart appliances in real time to optimal placement of PVs at the planning stage. The dissertation is composed of three parts: the literature review, the work that has already been done and the future research tasks. An overview on renewable energy generation and its challenges are given in Chapter 1. The overall literature survey, motivation and the scope of study are also outlined in the chapter. Detailed literature reviews are given in the rest of chapters. The overvoltage and undervoltage phenomena in typical distribution networks with integration of PVs are further explained in Chapter 2. Possible approaches for voltage quality control are also discussed in this chapter, followed by the discussion on the importance of the load management for PHEVs and appliances and its benefits to electric utilities and end users. A new real power capping method is presented in Chapter 3 to prevent overvoltage by adaptively setting the power caps for PV inverters in real time. The proposed method can maintain voltage profiles below a pre-set upper limit while maximizing the PV generation and fairly distributing the real power curtailments among all the PV systems in the network. As a result, each of the PV systems in the network has equal opportunity to generate electricity and shares the responsibility of voltage regulation. The method does not require global information and can be implemented either under a centralized supervisory control scheme or in a distributed way via consensus control. Chapter 4 investigates autonomous operation schedules for three types of intelligent appliances (or residential controllable loads) without receiving external signals for cost saving and for assisting the management of possible photovoltaic generation systems installed in the same distribution network. The three types of controllable loads studied in the chapter are electric water heaters, refrigerators deicing loads, and dishwashers, respectively. Chapter 5 investigates the method to mitigate overvoltage issues at the planning stage. A probabilistic method is presented in the chapter to evaluate the overvoltage risk in a distribution network with different PV capacity sizes under different load levels. Kolmogorov–Smirnov test (K–S test) is used to identify the most proper probability distributions for solar irradiance in different months. To increase accuracy, an iterative process is used to obtain the maximum allowable injection of active power from PVs. Conclusion and discussions on future work are given in Chapter 6