Islanding detection method using ridgelet probabilistic neural network in distributed generation

One of the challenging issues for a grid-connected embedded generation is to find a suitable technique to detect an islanding problem. The technique must be able to differentiate islanding from other grid disturbances and disconnect distributed generation (DG) rapidly to prevent from safety hazards, power quality issues, equipment damage, as well as voltage and frequency instability. This study proposes a Slantlet transform as a signal processing method to extract the essential features to distinguish islanding from other disturbances. A ridgelet probabilistic neural network (RPNN) is utilized to classify islanding and grid disturbances. A modified differential evolution (MDF) algorithm with a new mutation phase, crossover process, and selection mechanism is proposed to train the RPNN. The results of the proposed technique show its capability and robustness to differentiate between islanding events and other grid disturbance

Smart Distributed Generation System Event Classification using Recurrent Neural Network-based Long Short-term Memory

High penetration of distributed generation (DG) sources into a decentralized power system causes several disturbances, making the monitoring and operation control of the system complicated. Moreover, because of being passive, modern DG systems are unable to detect and inform about these disturbances related to power quality in an intelligent approach. This paper proposed an intelligent and novel technique, capable of making real-time decisions on the occurrence of different DG events such as islanding, capacitor switching, unsymmetrical faults, load switching, and loss of parallel feeder and distinguishing these events from the normal mode of operation. This event classification technique was designed to diagnose the distinctive pattern of the time-domain signal representing a measured electrical parameter, like the voltage, at DG point of common coupling (PCC) during such events. Then different power system events were classified into their root causes using long short-term memory (LSTM), which is a deep learning algorithm for time sequence to label classification. A total of 1100 events showcasing islanding, faults, and other DG events were generated based on the model of a smart distributed generation system using a MATLAB/Simulink environment. Classifier performance was calculated using 5-fold cross-validation. The genetic algorithm (GA) was used to determine the optimum value of classification hyper-parameters and the best combination of features. The simulation results indicated that the events were classified with high precision and specificity with ten cycles of occurrences while achieving a 99.17% validation accuracy. The performance of the proposed classification technique does not degrade with the presence of noise in test data, multiple DG sources in the model, and inclusion of motor starting event in training samples

A novel islanding detection technique using modified Slantlet transform in multi-distributed generation

In this paper, a new hybrid islanding detection scheme based on a combination of a modified Slantlet Transform (MSLT) and machine learning is applied to a passive time frequency islanding detection of multiple distributed generation units. A Harmony Search Algorithm (HSA) is used to optimally specify suitable scales of Slantlet transform and Slantlet decomposition levels for accurate islanding classification. Slantlet transform is utilized to derive the features in the required detection parameters measured from islanding and non-islanding events using identified Slantlet scales. In order to automate classification process, machine learning classifiers are utilized to detect islanding and non-islanding conditions with an objective of increasing the detection rate and avoiding nuisance distributed generation tripping during non-islanding situations. Islanding and non-islanding events are simulated for a multi-distributed generations system and used to assess the performance of the proposed anti-islanding protection method. The numerical results showing the efficiency of the proposed islanding detection technique are explained and conclusions are drawn

Harmonic Signature-Based One-Class Classifier for Islanding Detection in Microgrids

This article presents a new passive islanding detection technique in MGs that uses locally measured voltage signals at the PoC of DERs. The proposed method distinguishes islanding events from normal/non-islanding conditions by utilizing superimposed harmonic spectra extracted through a full-cycle discrete Fourier transform. Our solution utilizes a machine-learning-based one-class classifier to define and adjust thresholds for full harmonic spectra. Unlike other methods, our approach does not require data synchronization or communication infrastructure, nor does it suffer from common errors that often arise in current transformers. Moreover, our design is compatible with distributed and decentralized control strategies, as it relies solely on local voltage measurements at the PoC. Another advantage of this method is its low sampling frequency requirement, in the range of 1 kHz, making it cost-effective and implementable in most existing systems. In a comprehensive evaluation of a typical MG test system that included synchronous and inverter-based DERs, the proposed scheme demonstrated exceptional performance. Specifically, the scheme was able to detect 99.06% of different islanding events within the training range, with a detection time of just 10 to 21 ms. Additionally, the scheme remained 100% stable during various normal conditions, short-circuit faults, load changes, voltage changes, capacitor switching, and frequency changes.©2023 Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

GIS and Remote Sensing for Renewable Energy Assessment and Maps

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Energy Harvesting and Energy Storage Systems

This book discuss the recent developments in energy harvesting and energy storage systems. Sustainable development systems are based on three pillars: economic development, environmental stewardship, and social equity. One of the guiding principles for finding the balance between these pillars is to limit the use of non-renewable energy sources

Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation

One of the challenging issues for a grid-connected distributed generation is to find a suitable technique to detect an islanding problem. Islanding phenomena refers to the condition in which a distributed generation continues supplying power to a location at permissible voltage and frequency although electrical grid power from the electrical utility is no longer present. Its drawbacks can lead to issues such as power quality, safety of utility personal, and even power generation protection. Thus, the technique must be able to differentiate islanding from other grid disturbances and disconnect distributed generation rapidly to prevent from mentioned problems. In this work, a new islanding detection technique is proposed based on combination of Slantlet Transform and Ridgelet Probabilistic Neural Network to detect islanding conditions from other disturbance for a 250-kW PV array connected to a typical North American distribution grid and a wind farm power generation system. The proposed strategy includes several steps. In the first step, all possible detection parameters/ signals which can be affected by islanding occurrence in the system are measured locally for islanding and non-islanding conditions. Then, by means of the Slantlet Transform theory, the matrix data extraction for any decomposition level are computed and the best of them are selected as input data to feed to an effective classifier. Next, an advanced machine learning based on is utilized to predict islanding and none islanding states. In order to train Ridgelet probabilistic neural network, a modified differential evolution algorithm with new mutation phase, crossover process, and selection mechanism is introduced. Finally, the performance of the proposed methods is also assessed using the performance indicators such as various error measurement criteria, detection rate and false alarm and compared with recent works. Furthermore, to evaluate the efficiency of the proposed modified differential evolution for the training of ridgelet probabilistic neural network, four statistical search techniques, namely, particle swarm optimization, genetic algorithm, simulated angling, and classical differential evolution are used and their results are compared. Results show that the proposed method is able to detect islanding conditions with 100% accuracy, 100% detection rate and 0% false alarm with detection time of less than 0.19s. Non detection zone decreases to around zero and the proposed method has the ability to detect islanding up to 0.1% power mismatch. The error measurements of the proposed method such as Mean Absolute Percentage Error, Mean Absolute Error, And Root Mean Square Error for islanding detection are less than 0.02% for ideal and noisy conditions which shows that the algorithm is not sensitive to noise. Based on the results the proposed method is accurate in detecting islanding phenomena and effective in noisy conditions