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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.

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).

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.

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.

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.

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.

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.

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.