Search Results (7)

Industrial control systems (ICS) are critical networks directly linked to the value of core national and societal assets, yet they are increasingly becoming primary targets for numerous cyberattacks today. The ICS network, a fusion of operational technology (OT) and information technology (IT) networks, possesses a broad attack vector, and attacks targeting ICS often take the form of advanced persistent threats (APTs) exploiting zero-day vulnerabilities. However, most existing ICS security techniques have been adaptations of security technologies for IT networks, and security measures tailored to the characteristics of ICS data are currently insufficient. To mitigate cyber threats to ICS networks, this paper proposes an anomaly detection technique based on dynamic data abstraction. The proposed method abstracts ICS data collected in real time using a dynamic data abstraction technique based on noise reduction. The abstracted data are then used to optimize both the update rate and the detection accuracy of the anomaly detection model through model adaptation and incremental learning processes. The proposed approach updates the model by quickly reflecting data on new attack patterns and their distributions, effectively shortening the dwell time in response to APTs utilizing zero-day vulnerabilities. We demonstrate the attack response performance and detection accuracy of the proposed dynamic data abstraction-based anomaly detection technique through experiments using the SWaT dataset generated from a testbed of an actual ICS process. The experiments show that the proposed model achieves high accuracy with a small number of abstracted data while rapidly learning new attack pattern data in real-time without compromising accuracy. The proposed technique can effectively respond to cyberattacks targeting ICS by quickly learning and reflecting trends in attack patterns that exploit zero-day vulnerabilities. Full article

Compared to the conventional finite control set model predictive control (FCS-MPC), the double vector model predictive current control (DVMPCC) for permanent magnet synchronous motors (PMSMs) has a better steady-state performance without significantly increasing the switching frequency. However, determining optimal vectors with their dwell times requires a high computational burden. A low-complexity DVMPCC in the steady state was proposed in this study to address this problem. Firstly, the operating state of the motor was judged according to the speed error. During steady-state operation, the first optimal active vector was selected from three candidate vectors adjacent or identical to the active vector applied in the previous control period, reducing the number of comparisons by half. Next, the second optimal vector was selected from the other two active vectors, and the zero vector, the second optimal vector with the duty cycle, was determined according to the deadbeat condition of the q-axis current and cost function minimization. Finally, simulation and experimental results proved that the proposed low-complexity DVMPCC for surface-mounted permanent magnet synchronous motors is practical and feasible. Full article

Characterizing the electric energy curve can improve the energy efficiency of existing buildings without any structural change and is the basis for controlling and optimizing building performance. Artificial Intelligence (AI) techniques show much potential due to their accuracy and malleability in the field of pattern recognition, and using these models it is possible to adjust the building services in real time. Thus, the objective of this paper is to determine the AI technique that best forecasts electrical loads. The suggested techniques are random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), long short-term memory (LSTM), and temporal convolutional network (Conv-1D). The conducted research applies a methodology that considers the bias and variance of the models, enhancing the robustness of the most suitable AI techniques for modeling and forecasting the electricity consumption in buildings. These techniques are evaluated in a single-family dwelling located in the United States. The performance comparison is obtained by analyzing their bias and variance by using a 10-fold cross-validation technique. By means of the evaluation of the models in different sets, i.e., validation and test sets, their capacity to reproduce the results and the ability to properly forecast on future occasions is also evaluated. The results show that the model with less dispersion, both in the validation set and test set, is LSTM. It presents errors of −0.02% of nMBE and 2.76% of nRMSE in the validation set and −0.54% of nMBE and 4.74% of nRMSE in the test set. Full article

A space vector pulse width modulation (SVPWM) algorithm is an important part of the permanent magnet synchronous machine (PMSM) drive to achieve direct current (DC) to alternating current (AC) conversion. The execution of the conventional SVPWM algorithm is a complex process which will limit the sampling frequency of the high-speed PMSM drive. Low sampling frequency will cause high current total harmonic distortion (THD) and eddy current loss. To increase the sampling frequency, this paper proposes a novel simplified SVPWM algorithm. The proposed SVPWM algorithm turns the vector composition problem of the conventional SVPWM algorithm into an optimization problem of the dwell time of the basic vector. The proposed SVPWM algorithm has an optimal vector dwell time (OVDT). The dwell time of the basic vector can be directly calculated by solving the optimization problem. The proposed SVPWM algorithm does not need sector identification compared to the conventional algorithm. The experiments of the proposed SVPWM algorithm are performed in a high-speed PMSM drive of a flywheel energy storage system (FESS). Compared to the conventional SVPWM algorithm, the execution time of the proposed SVPWM algorithm is reduced by 38%. By using the proposed SVPWM algorithm, the sampling frequency can be increased from 33 kHz to 40 kHz. With the higher sampling frequency, the current THD is reduced by 25.6%. The effectiveness of the proposed simplified SVPWM algorithm is verified experimentally. Full article

The disadvantage of finite control set-model predictive control (FCS-MPC) is that the switching frequency is variable and relies on the sampling time and operating point. This paper describes how to implement a new algorithm to achieve a fixed-switching frequency functionality for the FCS-MPC. The used approach combines the FCS-MPC with the SVPWM, resulting in the calculation of dwell times and the selection of the best two active vectors for the next sample interval. These dwell times have a significant impact on FCS-MPC performance during transient and steady-state conditions, and their values are determined using various mathematical models. To solve the problem of the fixed-switching frequency with lower harmonics distortion compared to the conventional modulated MPC (M${}^2$PC), an ANN-based trained network is proposed to calculate the duty-cycle of the applied vectors and thus the dwell time in the next sampling interval. The ANN network receives the cost functions of the two active vectors and the zero vector from the M${}^2$PC control algorithm and determines the optimal duty-cycle for each vector based on a proper tuning. In this way, three goals are achieved, the first goal is that the algorithm explicitly obtains a fixed-switching frequency, and secondly, the cost is as simple as the conventional M${}^2$PC. Finally, the feature of including objectives and non-linearity is still applicable. The paper’s case study used the two level voltage source inverter (2L-VSI) for uninterruptible power supply (UPS) applications. The results based on MATLAB/Simulink revealed that the ANN-M${}^2$PC has retained all FCS-MPC features in addition to operating at a fixed-switching frequency, while the power quality is significantly enhanced. Full article

The three-phase H7 inverter topology installs an additional power semiconductor switch to the positive or negative node of the DC-link for reducing the common-mode voltage (CMV) by disconnecting the inverter from the DC source during the zero-voltage vectors. The conventional CMV reduction method for the three-phase H7 inverter uses modified discontinuous pulse width modulation (MDPWM) and generates a switching signal for the additional switch using logical operations. However, the conventional method is unable to eliminate the CMV for the entire dwell time of the zero-voltage vectors. It only has the effect of reducing the CMV in a limited area of the space vector where the $V_7$ zero voltage vector is applied. Therefore, this paper proposes an optimized modulation method that can reduce the CMV during the entire dwell time of zero-voltage vectors. The proposed method moves the switching patterns by adding an offset voltage to guarantee that only one kind of zero-voltage vector, $V_7$, is applied in the system. It then turns off the seventh switch only during the zero-voltage vector to disconnect the inverter from the DC source. As a result, the CMV and the leakage current are attenuated for the entire dwell time of the zero-voltage vector. Simulation and experimental results confirm the validity of the proposed method. Full article

This paper presents three phase current reconstruction methods for a three-level neutral point clamped inverter (NPCI) by measuring the voltage of a shunt resistor placed in the neutral point of the inverter. In order to accurately acquire the phase currents from the shunt resister, the dwell time of the active voltage vectors need to exceed the minimum time. On the other hand, if the time of active voltage is shorter than the minimum time, the current measurement becomes impossible. In this paper, unmeasurable regions for current are classified into three areas. Area 1 is a region in which both phase currents can be measure. Therefore, it is not necessary to restore the current. In Area 2, only one phase current can be measured. Thus, an estimation or restoration method is needed to measure another phase current. In this paper, the current estimation method using an electrical model of the motor is proposed. Area 3 is the region in which both phase currents can not be measured. In this case, it is necessary to move the voltage vector to the current measurable area by injecting the voltage. In this paper, Area 3 is divided into 36 sectors to inject optimal voltage. The proposed methods have the advantages of high current measurement accuracy and low THD (total harmonic distortion). The effectiveness of the proposed methods are verified through experimental results. Full article