Student psychology-based optimization-tuned cascaded controller for frequency regulation of a microgrid

This paper presents a student psychology-based optimization (SPBO)-tuned cascaded control scheme for an interconnected microgrid scenario. Generally, the different distributed energy sources are assembled to form the microgrid architecture, and the majority of the sources are environment-dependent. Furthermore, the intermittent power output from these sources causes a generation–load power mismatch, resulting in power and frequency oscillations. In this regard, the proposed student psychology-based optimization-tuned cascaded controller tackles the power-frequency mismatch issues under an interconnected microgrid scenario. Additionally, an improved power tie-line model is introduced considering the effect of line resistance in the microgrid scenario, as line resistance plays a significant role in power flow between the control areas. In addition, numerous case studies are investigated to examine the effectiveness of the proposed design methodology under the suggested control scheme. Furthermore, a detailed performance analysis is carried out considering the proposed model operation under a 12-node radial distribution network in order to examine the system compatibility in a practical distribution network. The obtained results ensure superior performances in terms of the system’s overall peak over/undershoots, oscillations, and settling time utilizing the proposed controller under the improved microgrid scenario

A novel nature-inspired nutcracker optimizer algorithm for congestion control in power system transmission lines

In the restructured power system, where uncertainties are common, managing congestion becomes a crucial aspect of power system operation and control. Congestion management aims to alleviate the power system transmission line congestion while meeting the system constraints at minimal cost. This research introduces a generation rescheduling method for congestion management in the electricity market, leveraging an innovative nutcracker optimizer algorithm. The nutcracker optimizer algorithm, inspired by nutcrackers’ food accumulation mechanisms, is a recently developed nature-inspired algorithm. The efficacy of this proposed approach is assessed across modified IEEE 30-bus, and IEEE 118-bus test systems, considering the system parameters. The effectiveness of the proposed congestion management with the nutcracker optimizer algorithm is analyzed by comparing its results with those generated by other recent optimization techniques. Results demonstrated that the nutcracker optimizer algorithm surpasses other comparative methods, requiring fewer fitness function evaluations, avoiding local optima, and displaying encouraging convergence traits. Implementing this approach can assist the system operators in swiftly addressing contingencies, ensuring secure and reliable power system operation within a deregulated environment

Measurement and Detection of Harmonic Sources for Radial and Non-radial Distribution Network

Now a day’s good quality of power (i.e harmonic free power) is as much as required as reliability of power supply. But the presence of large number nonlinear loads in the power system inject huge amount of unwanted harmonics in to the power network. Harmonic results malfunctioning of microprocessor and microcontroller-based equipment, overheating in neutral conductors, transformers or induction motors, deterioration or failure of PF (power factor) correction capacitors, improper operation of breakers and relays, pronounced magnetic fields near transformers and switchgear etc. In this paper a new method for detection of harmonic sources in distribution systems is proposed. The harmonic distortion power is used to determine the location of the polluting loads in this method. This harmonic distortion power (HDP) method compares the magnitudes of the harmonic distortion power, more specifically ratio of harmonic distortion power to the non active power at the point of common coupling (PCC) and detects the dominant source of harmonic pollution in the system. This method can also determine the harmonic contributions of a customer at the point of common coupling.

A New Approach to Detect Power Quality Disturbances in Smart Cities Using Scaling-Based Chirplet Transform with Strategically Placed Smart Meters

The growth of Internet of Things (IoT)-enabled devices has increased the amount of data created by the distribution network's periphery nodes, requiring more data transfer capacity. Recent applications' real-time requirements have strained standard computing paradigms, and data processing has struggled to keep up. Edge computing is employed in this research to detect distribution network faults, allowing for instant sensing and real-time reaction to the control room for faster investigation of distribution problems and power outages, making the system more reliable. Moreover, to overcome the challenges of fault detection, advanced signal processing methods need to be integrated with the Adaboost classifier. An Adaboost-based edge device, suitable for installation on top of a power pole, is proposed in this research as a means of real-time fault detection. To increase throughput, decrease latency and offload network traffic, data collecting, feature extraction and Adaboost-based problem identification are all performed in an integrated edge node. Enhanced detection accuracy (98.67%) and decreased latency (115.2 ms) verify the effectiveness of the suggested approach. In this research, we enhance the classical chirplets transform to create the scaling-basis chirplet transform (SBCT) for time-frequency (TF) analysis. This approach modulates the TF basis around the relevant time function to modify the chirp rate with frequency and time. By carefully selecting the sampling frequency, it is possible to discriminate between short circuit fault and high-impedance fault (HIF) by calculating spectral entropy. The TF representation obtained with the SBCT provides considerably higher energy concentrations, even for signals with numerous components, closely spaced frequencies and heavy background noise.</p

The reaction of heterocyclic amine with pendant naphthyl group in Pd(alpha/beta-NaiR)Cl-2 (alpha/beta-NaiR=1-alkyl-2-(naphthyl-alpha/beta-azo)imidazoles) and the product characterisation

The reaction of Pd(alpha/beta-NaiR)Cl-2 (1-alkyl-2-(naphthyl-alpha/beta-azo)imidazoles, alpha-NaiR (1) and beta-NaiR (2)) with m-aminopyridine (m-NH2-Py) and o-aminopyrimidine (o-NH2-Pym) in acetonitrile solution has synthesized a C-N coupled product, chloro[1-alkyl-2-{(7-imidophenyl)pyridyl-alpha/beta-azo}imidazole-N,N',N '']palladium(II), Pd(alpha/beta-NaiR-N-Py-m)Cl (3, 4) and Pd(alpha/beta-NaiR-N-2-Py-o)Cl (5, 6). The structural confirmation has been carried out by X-ray diffraction study. The solution electronic spectra of C-N fused products, 3-6, show transitions within 600-900 rim those are absent in Pd(alpha/beta-NaiR)Cl-2. Cyclic voltammogram shows four successive redox couples, one of them (positive to SCE) is oxidative in nature and others (negative to SCE) are ligand reductions. Emission is observed from ligand centred orbitals and has been ascribed to pi-pi* excitation process. The excited state decays following radiative and nonradiative biexponential routes. Absorption and fluorescence spectra of pyridylamine fusion product show H+ and metal ion (Zn2+, Cd2+) sensitivity.</p

Identification of Cross-Country Fault with High Impedance Syndrome in Transmission Line Using Tunable Q Wavelet Transform

The transmission lines of an electricity system are susceptible to a wide range of unusual fault conditions. The transmission line, the longest part of the electricity grid, sometimes passes through wooded areas. Storms, cyclones, and poor vegetation management (including tree cutting) increase the risk of cross-country faults (CCFs) and high-impedance fault (HIF) syndrome in these regions. Recognizing and classifying CCFs associated with HIF syndrome is the most challenging part of the project. This study extracted signal characteristics associated with CCF and HIF syndrome using the Tunable Q Wavelet Transform (TQWT). An adaptive tunable Q-factor wavelet transform (TQWT) based feature-extraction approach for CCHIF fault signals with high impact, short response period, and broad resonance frequency bandwidth was presented. In the first part, the time–frequency distribution of the vibration signal is used to determine the distinctive frequency range. Adaptive optimal matching of the impact characteristic components in the vibration signal was achieved by optimizing the number of decomposition layers, quality factor, and redundancy of TQWT based on the characteristic frequency band. In the last, the TQWT inverse transform was utilized to recreate the best sub-band to boost its weak impact characteristics. The effectiveness of the approach is confirmed by simulation and experimental findings in signal processing. The best decomposition level for signature features that can be extracted has been decided by Minimum Description length (MDL). The IEEE 39-bus system is used to test the suggested approach with reactor switching and the Ferranti effect