Analysis of Different Meta Heuristics Method in Intelligent Fault Detection of Multilevel Inverter with Photovoltaic Power Generation Source

Manjunath, T G; Kusagur, Ashok

doi:10.11591/ijpeds.v9.i3.pp1214-1222

Cited by 10 publications

(9 citation statements)

References 20 publications

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“…PSO tended to predict more accurately and faster than GA [42]. On the other hand, four metaheuristic algorithms including PSO, GA, tabu search and cuckoo search have been applied to optimize the weight and the bias values of an ANN in a multilevel inverter fault diagnosis application [43]. A modified evolutionary PSO with time varying acceleration coefficient (MEPSO-TVAC) was proposed to optimize regression coefficient of the ANN before performing transformer fault detection [44].…”

Section: Ann and Evolutionary Neural Network Methods For Condition Monitoring And Fault Detectionmentioning

confidence: 99%

Hybridization of harmonic search algorithm in training radial basis function with dynamic decay adjustment for condition monitoring

2021

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In recent decades, hybridization of superior attributes of few algorithms was proposed to aid in covering more areas of complex application as well as improves performance. This paper presents an intelligent system integrating a Radial Basis Function Network with Dynamic Decay Adjustment (RBFN-DDA) with a Harmony Search (HS) to perform condition monitoring in industrial processes. An effective condition monitoring can help reduce unexpected breakdown incidents and facilitate in maintenance. RBDN-DDA performs incremental learning wherein its structure expands by adding new hidden units to include new information. As such, its training can reach stability in a shorter time compared to the gradient-descent based methods. By integrating with the HS algorithm, the proposed metaheuristic neural network (RBFN-DDA-HS) can optimize the RBFN-DDA parameters and improve classification performances from the original RBFN-DDA by 2.2% up to 22.5% in two benchmarks and a real-world condition-monitoring case studies. The results also show that the proposed RBFN-DDA-HS is compatible, if not better than, the classification performances of other state-of-art machine learning methods.

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Section: Ann and Evolutionary Neural Network Methods For Condition Monitoring And Fault Detectionmentioning

confidence: 99%

Hybridization of harmonic search algorithm in training radial basis function with dynamic decay adjustment for condition monitoring

2021

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“…After the application of basic operators, advanced and problem specific operators are used. Based on the population statistics, the crossover and mutation probabilities are changed [25]. This process is repeated until the number of generations is less than or equal to maximum number of generations.…”

Section: Enhanced Genetic Algorithms (Ega)mentioning

confidence: 99%

Optimal power flow based congestion management using enhanced genetic algorithms

Kim

Salkuti

2019

IJECE

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<p>Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach.</p>

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“…In [18,19], a combination of soft computing technique and a simplified rule based algorithm is adopted to detect an open-circuit fault in switches for symmetric operation of CHB-MLI. [20] proposes an optimization based neural network for development of FDI scheme for CHBMLI. In [21] an neural network based FDI scheme has been discussed for the asymmetric operation of CHBMLI.…”

Section: Introductionmentioning

confidence: 99%

Fault detection technique for modified cascaded half-bridge multi-level inverter with polarity changer in pv grid system

Vyas

Shah

Srivani³

2021

SN Appl. Sci.

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Multilevel Inverters (MLI) are a viable option for a filter-less and transformerless photovoltaic system for direct grid integration, reducing losses, space and cost provided the issues of control and reliability are resolved. A modified cascaded half-bridge MLI with polarity changer is proposed with a reduced number of switches, thereby reducing control complexity. The proposed converter is designed for both seven-level and nine-level topologies. Selective harmonic elimination has been adopted to switch converter and the transcendental equations are solved by the gravitational search algorithm. The THD for nine-level configuration is 7.94% and 5.86% with MPPT and DC source inputs, respectively. The analysis on the open-circuit fault of switches confirms the presence of only five critical switches irrespective of the number of increase in levels, thereby requiring only five redundant switches at the Polarity Changer. The output voltage waveform is subjected to multiresolution analysis for feature extraction of voltages under various irradiance and temperature conditions. A conditions based fault detection algorithm is developed based on the observations of energies of signals to detect the open circuit fault in switches. Based on the comparative analysis, the proposed converter has fewer controlled switches for nine levels or higher configurations than other topologies. Apart from this, the high reliability due to inherent fault isolation capability restricted to half-bridge compared to different MLI topologies shows its superiority. For a nine-level MLI proposed topology reduces the cost considering redundancies for FDI in range of 67–30% depending on the topology for comparision. The system is simulated using MATLAB –Simulink and further validated by experimental results.

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Analysis of Different Meta Heuristics Method in Intelligent Fault Detection of Multilevel Inverter with Photovoltaic Power Generation Source

Cited by 10 publications

References 20 publications

Hybridization of harmonic search algorithm in training radial basis function with dynamic decay adjustment for condition monitoring

Hybridization of harmonic search algorithm in training radial basis function with dynamic decay adjustment for condition monitoring

Optimal power flow based congestion management using enhanced genetic algorithms

Fault detection technique for modified cascaded half-bridge multi-level inverter with polarity changer in pv grid system

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