Grouping Capacitor Voltage Estimation and Fault Diagnosis With Capacitance Self-Updating in Modular Multilevel Converters

Wang, Zhen

doi:10.1109/tpel.2020.3011131

Cited by 34 publications

(11 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1: hh q y l from the noise observation value at time h According to the law of large numbers, when the number of noise particles is very large, the particle filter is approximate to the posterior probability of the patrol inspection image quality state [11]. Namely:…”

Section: A Noise Elimination Methods For Inspection Image Of Substati...mentioning

confidence: 99%

An Early Warning Model for Intelligent Operation of Power Engineering based on Kalman Filter Algorithm

Shi¹,

Li²,

Sun³

et al. 2023

IJACSA

View full text Add to dashboard Cite

The accurate early warning of intelligent operation of power engineering can find the abnormal operation of substation equipment in time and ensure the safe operation of substation equipment. Thus, an early warning model for intelligent operation of power engineering based on Kalman filter algorithm is constructed. In this model, the noise elimination method of substation equipment inspection image based on particle resampling filter algorithm is introduced. After removing the noise information of operation situation inspection image of substation equipment, the gradient direction histogram feature, lab color space feature and edge contour feature in the image are extracted by the multi-feature extraction method for intelligent operation of power engineering based on multi-feature fusion. These features are combined to form the feature description set of equipment operation situation. The feature description set is used as the identification attribute set of the anomaly identification and early warning model for intelligent operation of electric power engineering based on Kalman filter algorithm to complete the anomaly identification and early warning of equipment operation situation. The test shows that when the model is used to observe the temperature change trend of the top layer of the transformer, the temperature error is very small, and the early warning accuracy for the abnormal temperature of the top layer of the transformer is very high, so the abnormal operation of the substation equipment can be found in time.

show abstract

Section: A Noise Elimination Methods For Inspection Image Of Substati...mentioning

confidence: 99%

An Early Warning Model for Intelligent Operation of Power Engineering based on Kalman Filter Algorithm

Shi¹,

Li²,

Sun³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…In their implementation, they perform operations among matrices, increasing the computational burden for the central controller. Indeed, as discussed in [52], the algorithm complexity of the KF proposed in [49]- [51] is O(m 2.376 + n 2 ), where m is the observation dimension, and n the number of states, which makes its implementation in MMCs with high number of SMs difficult. Thus, distributed KF-based observers can be an effective solution to overcome this issue.…”

Section: Proposed Kalman Filter-based Methods For Detecting Fdias And...mentioning

confidence: 99%

Cyber-Attacks in Modular Multilevel Converters

Burgos-Mellado

Donoso

Dragičević

et al. 2022

IEEE Trans. Power Electron.

View full text Add to dashboard Cite

Distributed control of modular multilevel converter (MMC) submodules (SMs) offers several potential benefits such as flexibility, scalability and modularity. In this approach, lowlevel control tasks, such as capacitor voltage balancing, can be distributed amongst controllers placed in the SMs. This decreases the computational burden for the central control system that performs high-level control tasks; also, a single point of failure is avoided. Distributed control architecture requires a cyberphysical network (CFN) through which local controllers share all the information necessary to perform their respective control loops. To date, none of the reported works in this field have paid attention to potential imperfections in the CFN. Indeed, previous works are based on the assumption that the network always provides correct information to the local controllers. However, erroneous measurements in the CFN may degrade the distributed control scheme operation, leading to suboptimal or even unstable operation. These events can occur in the presence of cyberattacks, for example, which can be created through illegitimate data intrusion into the distributed control architectures. This paper is the first to investigate the impacts of cyber-attacks on distributed control schemes used in MMCs. The effects of a specific cyber-attack, named false data injection attack (FDIA), on a consensus-based distributed control strategy are studied in this work. Additionally, a method for detecting FDIAs is proposed, along with a countermeasure strategy, to ensure the safe operation of the MMC whilst the attack is cleared. The proposals reported in this paper are validated using simulation and experimental results.

show abstract

“…The same relation between SM voltage and capacitor current was used in [19], where the error of the capacitance estimation was minimized by recursive least squares (RLS) estimation. Another strategy for a robust estimation is proposed in [20], by employing a Kalman lter. The method proposed in [21] also uses RLS for capacitance estimation, based on modeling the relation between the capacitance and SM voltage change during the fundamental period, when the nearest level modulation control is used.…”

Section: ) Parameter Changesmentioning

confidence: 99%

Hybrid Condition Monitoring for Power Converters: Learning-Based Methods With Statistical Guarantees

et al. 2023

View full text Add to dashboard Cite

In this paper, a new learning-based method is proposed for the early detection of changes of parameters in power converters. It circumvents the pertinent shortcomings of previous model-based methods, such as their need for acquiring switching signals, dependence on the control method, or the need for isolating a part of the system during monitoring and thus interfering with the system performance or its start-up time. Additionally, a downside of the learning-based approaches, which is that their performance depends on the quality of the measurements, is addressed through constructing hybrid models that combine the benets of both lines of development. Our approaches are evaluated with several types of features based on wavelet decomposition and empirical mode decomposition. Using these, an ANN-based classier is trained for fault detection. For achieving the nal decision on the presence or absence of a fault state, we propose the use of sequential hypothesis testing. This hybrid approach yields a much higher reliability than instantaneous ANNbased classication, while allowing for a statistically sound cross-temporal information integration and providing a controllable error bound for the probability of misclassications. The proposed method was evaluated on two data-sets recorded from a buck converter and an arm of a modular multilevel converter. The results show that, for both systems, the proposed method is capable of reliably recognizing changes of the system parameters. The potential for practical applications is shown through an implementation on a low-power-consumption microcontroller. INDEX TERMS Fault diagnosis, power converters, DC/DC Converter, MMC, machine learning, signal processing, hybrid models I. INTRODUCTIONC ONDITION monitoring of power converters in energy systems is of great importance, considering the wide range of applications in which they play a critical role, and their long operational time. While there are many dierent power converter topologies, these broadly share the same principles of operation via active and passive elements. Hence, very similar condition monitoring methods can, with some modications, be applied for dierent power converters.The specic condition monitoring technique proposed in this paper is applied to two dierent power converter topologies. The rst is a buck converter, a widely employed DC-DC power converter [1]. The second system is an arm of a of modular multilevel converter (MMC). These converters have gained great interest in academia as well as industry shortly after they were initially proposed [2], due to their modular design, their

show abstract

Grouping Capacitor Voltage Estimation and Fault Diagnosis With Capacitance Self-Updating in Modular Multilevel Converters

Cited by 34 publications

References 35 publications

An Early Warning Model for Intelligent Operation of Power Engineering based on Kalman Filter Algorithm

An Early Warning Model for Intelligent Operation of Power Engineering based on Kalman Filter Algorithm

Cyber-Attacks in Modular Multilevel Converters

Hybrid Condition Monitoring for Power Converters: Learning-Based Methods With Statistical Guarantees

Contact Info

Product

Resources

About