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In contemporary power systems, harmonics as undesired electrical phenomena, have drawn considerable attention of the research community investigating in design and development of optimal electrical networks. Harmonics affect the power electronics devices with a number of negative impacts, including higher power losses, worse power quality, and interference with other delicate grid connected equipment. An objective function of inter- and sub-harmonics is constructed for joint estimation of amplitude and phase with known frequency. Accurate estimation of the harmonics model is carried out with stupendous knacks of raptor-inspired Harris Hawks optimization algorithm (HHOA), a revolutionary population-based, nature-motivated optimization approach. The primary sources of inspiration for HHOA are Harris Hawks cooperative demeanor and surprise pounce chasing style in nature. The HHOA’s performance is assessed for harmonics parameter estimation with several noise conditions, differing particle sizes and iterations. The proposed HHOA-based estimation technique provides accurate, convergent and robust performance for harmonics estimation. The reliable level of fitness is achieved consistently in the range of 10⁻³ to 10⁻¹⁰, an evidence of valuable precision, stability and robustness of the proposed algorithm.

Three-phase active rectifiers based on the voltage source converter topology can successfully replace traditional thyristor-based rectifiers or diode bridge plus chopper in interfacing dc-systems to the grid. However, if the application in which they are employed has a high safety issue or if there are other loads connected to the same Point of Common Coupling (PCC), and sensitive to the harmonics produced by the switching of the converter, an LCL-filter has to be used. This kind of solution has been studied in theory but its sensitivity to the value of the LCL-filter passive elements employed, to the grid side stiffness and to the parameters of the controller has never been considered in detail. In this paper the experimental results of an LCL-filter-based three-phase active rectifier are analyzed with the circuit theory approach. A "virtual circuit" is synthesized in role of the digital controller and of the feedback filters to have an homogenous model that allows a sensitivity analysis which is rigorous and straightforward for the industry.

Smart Grid is expected to provide a reliable power supply with fewer and briefer outages, cleaner power, and self-healing power systems through advanced Power Quality (PQ) monitoring, analysis and diagnosis of the PQ measurements and identification of the root cause, and timely automated controls. It is important to understand that signal processing has been an integral part of advancing and expanding the horizons of this PQ research significantly and the capabilities and applications of signal processing for PQ are continually evolving. This paper thus presents a survey on the proven and emerging signal applications for enhancing PQ, focusing on algorithms for estimating system modal parameters because resonant frequencies and their damping information are critical signatures in evaluating the PQ. In particular, we discuss the need for investigating time-varying and nonlinear characteristics of the modal parameters due to dynamic changes in system operating conditions and introduce promising signal processing techniques for this purpose.

This paper deal with soft computing techniques such as artificial neural network (ANN), fuzzy logic controller (FLC) and proportional-integral (PI)-based static series voltage regulator (SSVR) for constant speed prime mover driven self excited induction generator (SEIG) feeding three phases linear and nonlinear loads. The constant speed prime mover such as biogas, biomass and gas-turbine driven standalone generating system has problem of poor voltage regulation. The SSVR is injects series voltage in to system to maintain constant source and load voltage with power quality improvement of source and load current. The SSVR is modeled using insulated gate bipolar junction transistor (IGBT)-based voltage controlled-voltage source converter (VC-VSC) with self supported DC bus. The A 7.5 kW, 415 V, 50 Hz asynchronous machine with voltage regulator and loads are designed, modeled and simulated in MATLAB environment. Simulated results are presented capability of an SEIG system with ANN, fuzzy and PI-based SSVR and their comparison.

Power Quality Assessment (PQA) is a critical issue both in transmission and distribution networks. Therefore, it is necessary to precisely classify the disturbances in shortest possible time to prevent the malfunction or increase of losses in the electrical equipment through appropriate remedial techniques. This paper proposes a highly accurate method of PQA through data acquisition using smart sensors, the Rogowski coils (RCs). RCs with wide band width and linear characteristics allow faithful reproduction of high-frequency (HF) signals. In the proposed method, simulated disturbance signals are applied to RC. The output signals are subjected to multilevel wavelet decomposition and then computation of the energy difference in the detailed components between the disturbance signal and the pure sinusoidal waveform is performed to design a fuzzy logic Power Quality Classifier. The classifier is tested by varying the magnitude, frequency and duration of the disturbance and found to be accurate to 98.38%. The classification accuracy depends mainly on the performance of sensors at HFs. Thus, with RCs as sensors instead of conventional instrument transformers, it is found that the precision of power quality classification is greatly improved.

This paper presents the design and analysis of a polygon-connected autotransformer based 20-pulse AC–DC converter which supplies direct torque-controlled induction motor drives (DTCIMDs) in order to have better power quality conditions at the point of common coupling. The proposed converter output voltage is accomplished via two paralleled 10-pulse AC–DC converters each of them consisting of five-phase diode bridge rectifier. An autotransformer is designed to supply the rectifiers. The design procedure of magnetics is in such a way that makes it suitable for retrofit applications where a six-pulse diode bridge rectifier is being utilized. The proposed structure improves power quality criteria at AC mains and makes them consistent with the IEEE-519 Standard requirements for $\mathrm{\text{SCR}}>20$. Furthermore, near-unity power factor is obtained for a wide range of DTCIMD operations. A comparison is made between six-pulse and proposed converters from the viewpoint of power quality indices. Results show that input current total harmonic distortion (THD) is less than 8% for the proposed topology at variable loads. A laboratory prototype of the proposed polygon-connected autotransformer-based 20-pulse AC–DC converter is developed and test results are presented to validate the developed design procedure and the simulation models of this AC–DC converter under varying loads.

Analysis, design and simulation of 126W power supply with better power quality are presented in the proposed work to run an auditorium light emitting diode (LED) light operating at universal AC input mains (90–270V). A single-ended primary inductance converter (SEPIC) topology is designed and driven in continuous conduction mode (CCM) with advance feedback system to maintain constant voltage at output. A proportional integral (PI) controller is also proposed to make the system stable, and stability analysis is discussed in detail with the help of transfer function derived from the state space model. Bode, Nyquist and Polar plots are clearly drawn using the MATLAB tool to claim the system stability. For justification of mathematical analysis, a simulation of the proposed LED driver is also performed in MATLAB–Simulink using sim-power toolbox. The simulation results show the improved value of power quality indices like power factor (PF), total harmonic distortion (THD) and crest factor (CF) with constant rating of 84V, 1.5A at output. Improved PF and reduced THD are under the limit of international standards like IEC-61000-3-2 Class C requirement.

In this paper, a novel seven-level Quasi-Z-Source-based $T$-type inverter (7LqZST${}^2\text{I}$) is proposed. The proposed inverter is an upgrade of Quasi-Z-source (qZs) network and seven-level $T$-type inverter. The 7L qZST${}^2\text{I}$ comprises of three qZs-based impedance networks, two bidirectional switches and an $H$-bridge inverter. It owns the advantages of reduced switch count, improved output voltage gain, enhanced reliability and better quality of output voltage and current. The performance of the proposed topology is tested for two different pulse width modulation techniques based on shoot-through control. The first technique offers simple control and operated at a fixed shoot-through duty cycle for realizing output voltage level. The second technique facilitates independent control of each qZs network dc-link voltage and they can be operated at different shoot through duty cycle which overcomes the limitation of first technique with better quality in output voltage. The detailed operation of the proposed topology and control schemes have been elaborated for different switching states for each output voltage level generation. Extensive simulation and experimentation are performed for both the switching schemes to verify their performance under steady state and dynamic conditions. Furthermore, a brief comparison is constructed to highlight the merits of the proposed inverter with conventional topologies.

Power quality issues and their effective mitigation invariably play a crucial role in a microgrid system. Such power quality problems are often resolved by employing multiple power electronics-based components in the utility grid. This paper is focused on the optimal enhancement of power quality under islanded mode of operation in a microgrid, with a deep Convolutional Neural Network (CNN) with Long Short-term Memory (LSTM) algorithm using distribution static compensator (DSTATCOM). The objective of the research is centered on the reactive power control in DSTATCOM using deep CNN with LSTM for voltage enhancement, minimization of current distortion and reduction of harmonics on a microgrid. This objective can be achieved by the proposed Simulink design model of DSTATCOM intended for improving the power quality in a microgrid. The renewable energy-based power compensator is used for an enhanced and effective control strategy like voltage and current control of the microgrid circuit and uses LSTM-based deep CNN for achieving superior time consumption indicators. Due to varying loads in the microgrid, the reactive power and harmonic voltage and current may be distorted. This problem can be rectified by controlling the microgrid using the LSTM-based deep CNN. This approach consequently reduces the negative-sequence frequency range with the aid of this filtering method in the proposed microgrid circuit. The microgrid is thereafter subjected to different testing conditions and the corresponding simulation results are discussed in relation to existing approaches. The proposed framework was observed to have successfully accomplished harmonic substance and voltage profile enhancement.

Solar PV-connected distributed utility grid often faces several issues due to variable penetration of the generated power. It creates frequent disturbance in load side and increases the voltage instability. It is a great challenge to maintain the stability at distributed low-voltage grid and improve the quality of power. In order to overcome this problem, this paper proposes an adaptive voltage and current regulatory approach to improve the power quality in a solar PV-integrated low-voltage utility grid. It supplies auto-adjustable reactive power during the small and large voltage deviations in the grid. The proposed approach assures that the load bus voltage is maintained at 1 p.u. under variable environmental conditions. In addition, the power quality gets improved by injecting the power with improved quality. Three cases of standalone mode, grid-connected modes with and without STATCOM have been investigated and reported in this paper. To validate the proposed adaptive voltage and current regulatory approach, the dynamic results of regulated grid voltage under poor environmental conditions are analyzed and the measured results are presented in this paper. Furthermore, the obtained results are evaluated with the existing approaches such as BAT, firefly and elephant herding optimization (EHO) algorithms and reported in this paper.

To avoid the negative effects of using a control signal with a ripple, which is generated by the feedback of measured active power filter (APF) variables, a nonlinear observer is employed in this paper. The observer design, through the use of exact TS models and Lyapunov-based LMI conditions, is achieved. Both the APF output current and the DC voltage are estimated by the observer, and they are used in the cascade control feedback. In this way, high gains in the inner control loop are employed, giving place to a control signal without undesired harmonic components or overmodulation. This allows an APF performance improvement for compensation tasks and for reducing the undesired components injection to the mains. A simulation and experimental comparison between APF results using observer and APF results without using observer is presented. Better results are achieved for the observer version case, reducing the THD from 47.6% to 4.8% in experimental conditions, satisfying the IEEE Standard 519^TM-2014. Also, load change tests are carried out, where the stability of the system is kept. Moreover, by using the observer, a DC voltage sensor was not required, reducing the number of system sensors.

Multi-level Inverters (MLIs) are increasingly employed in grid-connected Photovoltaic (PV) systems to optimize power quality. The use of MLIs offers several benefits over traditional inverters, including improved output waveform quality, reduced Total Harmonic Distortion (THD) and increased efficiency. However, the control of MLI is complex, and a precise control system is required to ensure that the system operates at optimal conditions. Therefore, an Adaptive Chaotic Mapping Philippine Eagle Optimization (ACM-PEO) algorithm-based Three degrees of freedom Fractional Order Proportional Integral Derivative with Dual Filter (3DOF-FOPID²-DF) controller is proposed. The dual derivative and dual filter component introduced in the 3DOF-FOPID controller improve the transient response of the system even under sudden load changes. The ACM-PEO algorithm further enhances the system performance of the 3DOF-FOPID²-DF controller by optimizing the parameter gains. The proposed ACM-PEO-based 3DOF-FOPID²-DF control technique is simulated in MATLAB and the simulation results demonstrate that it has improved transient and dynamic response, reduced THD, faster convergence and better tracking of the reference signal. These benefits enhance the efficiency and reliability of the systems.

This paper discusses the definition of active and reactive power in wavelet domain. Different definitions for three-phase power may cause significant changes in the measurement equipment topology. The simple phase shift using the Akagi method between current and voltage for reactive power measurement is discussed. A new power quality analysis technique (pqAT) is proposed.

Power quality improvement is one of the central and critical needs in all types of power-driven industries for perfect utilization of sources. However, fundamental inconveniences in power quality have been recognized, for instance, sags, swells, harmonic distortions and different blocks. Among those, sags and swells are overwhelmingly uncovered, and each influences the electrical gadgets or electrical machines and this requires to be compensated with an advancement to ensure any mal-operation or disappointment. To settle these troubles, custom power gadgets are used like unified power quality conditioner (UPQC), dynamic voltage restorer (DVR) and Distributed Static Compensator (DSTATCOM). This work aims to update the power quality in both single-phase and three-phase system using UPQC. The capacity of UPQC to mitigate voltage sag, voltage swell, responsive power remuneration, load balancing, zero voltage direction, and power factor redress and Total Harmonics Distortion (THD) in the appropriation framework is done by utilizing adaptive distributed power balanced control (ADPBC) method. This investigation works on the single-phase UPQC design to utilize two H-connect setups with eight switches and the three-phase UPQC arrangement to use four legs and three-phase four wire course of action. The overall function of the UPQC with distributed generation (DG) system will be able to compensate for the offset voltage and current disturbances with all the compensated data being collected and verified in Internet of Things (IoT) based on conditional Random Fields (CRFs).

Renewable energy sources connected in distribution systems utilizing power electronics devices to interface lead to various power quality problems. This chapter presents a review on power quality issues associated with the grid-connected renewable energy systems and mitigation techniques. To mitigate the power quality issues, an effective role is played by power electronic devices and custom power devices such as active power filters (APFs) and flexible AC transmission systems (FACTs). This chapter also discusses IEC and IEEE standards for grid-connected renewable energy system.

Power quality disturbance identification and classification are important for power quality management, for which a new method is proposed to deal with power quality disturbance identification and classification based on GA-ELM (Genetic Algorithm-Extreme Learning Machine). Decompose the disturbance signals with wavelet for ten layers, it is proposed that using standard deviation and mean of the energy difference, and the ratio of them as the feature vectors. In addition, the root mean of disturbance signals and normal signals is calculated as a supplement in order to reduce the dimension of the importing vectors. The Input Layer and the Hidden Layer Connection Weights and Hidden Layer Neuron Threshold which are set randomly in ELM can influence classification accuracy. So it is proposed that ELM training error is used as the fitness function of GA to optimize Input Layer and the Hidden Layer Connection Weights and Hidden Layer Neuron Threshold to enhance the accuracy of classification and also maintain high classification speed. Simulation results demonstrate that this method can accurately and effectively identify seven common disturbance types.

There has been a significant change to the domestic loads in recent years. With power electronic technique being more and more advanced and popular, an increasing number of domestic electrical appliances adopting power electronic devices are used and connected to low voltage distribution networks, which raises the harmonic level, resulting to a negative impact on the power quality. In this paper, domestic loads are classified into different categories according to simplified circuit topology. Furthermore, suggestions on how to improve power quality in networks are put forward.

To overcome the deviation caused by the effect of subjective factors in power quality evaluation, and make the evaluation more accurate and objective, on the basis of power quality comprehensive evaluation criterion, the projection data are built with projection pursuit method. Two hundred power quality comprehensive evaluation samples are randomly generated within the range of each class. The projection pursuit model is built by unitary processing according to these samples, which converts the evaluation into non-linear multi-constrained optimization problems. The optimal projection direction vector and the weight coefficient of each evaluation index are obtain with composite simplex method. Meanwhile the corresponding relationship between region of variation of projection eigenvalue and classification grade is established. The precision of sample evaluation reached 100% and the evaluation of the measured data achieved good effect. Thus, the scientific evaluation of power quality is realized by the projection pursuit model.

To improve accuracy and speed of a power quality monitoring device, the monitoring device with field-programmable gate array (FPGA) as the core of processing and controlling is designed. The hardware of the device includes signal acquisition unit, communication unit, power supply unit, etc; the software includes an NIOS II embedded core, optimizing the flow of data processed parallel, and Phase-locked frequency multiplication module and FFT harmonic analysis module implementing the synchronous sampling and analysis of power quality data are built. Test results show that the device has advantages of fast response, high accuracy and satisfactory real-time performance, and conforms to the national standards of power quality monitoring.

In this paper, a set of visual evaluation method based on the visualization technology is presented to evaluate power quality of power network in coal mine. Firstly, according to the characteristics of 10kV network, 14 indexes of voltage quality, current quality and power factor are selected to establish the power quality evaluation system of power network in coal mine. Then, parallel coordinates is used to delete two indexes of the evaluation results in order to reduce the follow-up calculation. Finally, using radar plot to evaluate power quality of power grid in coal mine, a combination weighting method based on sensitivity analysis and IOWA operator is put forward to mix many kinds of subjective and objective weight together in order to improve the accuracy of evaluation results. Through the actual evaluation for power quality of power grid in four coal mines, the effectiveness of the method is verified by the evaluation index system and the evaluation method.