Improved Brain Storm Optimization with Differential Evolution strategies for Load Adjustment Distribution State Estimation Using Correntropy

Azuma, Daichi; Fukuyama, Yoshikazu; Jintsugawa, Toru; Fujimoto, Hisashi; Matsui, Tetsuro

doi:10.1016/j.ifacol.2019.08.251

Cited by 6 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As it was highlighted at the beginning of this section, the range of SE methods published in the literature is broad. Besides conventional solutions, large emphasis has been placed on the use of artificial intelligence: fuzzy logic [74,75], artificial neural network [76][77][78][79], particle swarm optimization [80,81], evolutionary algorithms [82], interior point optimization and brain storm optimization [83], biogeography based optimization [84], firefly algorithm [85], Kalman-filters [73,[86][87][88][89][90] and advanced techniques (forecastaided SE [50,73], multi-area SE [14,63,67,68,[91][92][93], and event triggered approaches [94]).…”

Section: Application Constraints Of the Algorithmsmentioning

confidence: 99%

“…The method is presented to be robust against low redundancy measurement sets. A similar approach is used in [83] to create a framework for SE, using on-line and off-line computing, and measurement data of different temporal resolution. Combining low-resolution advanced metering infrastructure (AMI) data with other observations is also proposed by [40,[97][98][99], which discusses the general data protection regulation (GDPR) aspects of using smart meter data for SE, concluding that voltage measurements from smart meters can be seen as non-private data.…”

Section: Application Constraints Of the Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review

et al. 2021

View full text Add to dashboard Cite

Global trends such as the growing share of renewable energy sources in the generation mix, electrification, e-mobility, and the increasing number of prosumers reshape the electricity value chain, and distribution systems are necessarily affected. These systems were planned, developed, and operated as a passive structure for decades with low level of observability. Due to the increasing number of system states, real time operation planning and flexibility services are the key in transition to an active grid management. In this pathway, distribution system state estimation (DSSE) has a great potential, but the real demonstration of this technique is in an early stage, especially on low-voltage level. This paper focuses on the gap between theory and practice and summarizes the limits of low-voltage DSSE implementation. The literature and the main findings follow the general structure of a state estimation process (meter placement, bad data detection, observability, etc.) giving a more essential and traceable overview structure. Moreover, the paper provides a comprehensive mapping of the possible use-cases state estimation and evaluates 27 different experimental sites to conclude on the practical applicability aspects.

show abstract

Section: Application Constraints Of the Algorithmsmentioning

confidence: 99%

Section: Application Constraints Of the Algorithmsmentioning

confidence: 99%

The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review

et al. 2021

View full text Add to dashboard Cite

show abstract

Fault Diagnosis of Oil-Immersed Power Transformer Based on Difference-Mutation Brain Storm Optimized Catboost Model

Zhang

Chen

Zhang³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

To address the problem of low accuracy of power transformer fault diagnosis, this study proposed a transformer fault diagnosis method based on DBSO-CatBoost model. Based on data feature extraction, this method adopted DBSO (Difference-mutation Brain Storm Optimization) algorithm to optimize CatBoost model and diagnose faults. First, for data preprocessing, the ratio method was introduced to add features to the original data, the SHAP (Shapley Additive Explanations) method was applied for feature extraction, and the KPCA (Kernel Principal Component Analysis) algorithm was employed to reduce the dimension of data. Subsequently, the preprocessed data were inputted into the CatBoost model for training, and the DBSO algorithm was adopted to optimize the parameters of the CatBoost model to yield the optimal model. Lastly, the DBSO-CatBoost model was exploited to diagnose the transformer fault and output the fault type. As indicated from the example results, the accuracy of the transformer fault diagnosis based on DBSO-Catboost model could be 93.71%, 3.958% higher than that of CatBoost model and significantly exceeding that of some common models. Furthermore, compared with other preprocessing methods, the accuracy of fault diagnosis by employing the data preprocessing method proposed in this study was significantly improved.

show abstract

Dependable Parallel Multi-population Global-best Brain Storm Optimization with Differential Evolution strategies for Distribution System State Estimation using Just-in-time Modeling and Correntropy in Power Systems

Azuma

Fukuyama

et al. 2020

2020 IEEE Congress on Evolutionary Computation (CEC)

View full text Add to dashboard Cite

Improved Brain Storm Optimization with Differential Evolution strategies for Load Adjustment Distribution State Estimation Using Correntropy

Cited by 6 publications

References 11 publications

The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review

The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review

Fault Diagnosis of Oil-Immersed Power Transformer Based on Difference-Mutation Brain Storm Optimized Catboost Model

Dependable Parallel Multi-population Global-best Brain Storm Optimization with Differential Evolution strategies for Distribution System State Estimation using Just-in-time Modeling and Correntropy in Power Systems

Contact Info

Product

Resources

About