Elektronika ir Elektrotechnika

Decoupled Unknown Input Observer for Takagi-Sugeno Systems: Hardware-in-the-Loop Validation to Synchronous Reluctance Motor

2024-03-28T17:14:57+02:00

This paper introduces a decoupled unknown input observer (DUIO) for Takagi-Sugeno (T-S) systems, designed specifically for the synchronous reluctance motor (SynRM). The proposed DUIO method demonstrates enhanced robustness and accuracy in state estimation by effectively decoupling the influence of unknown inputs from the estimation error dynamics. Furthermore, the DUIO exhibits superior performance compared to the proportional integral observer (PIO) and the proportional multi-integral observer (PMIO) presented in previous studies, without the need for prior knowledge of the unknown input form or assumptions regarding its boundedness. Stability conditions, achieved using the quadratic Lyapunov function, are expressed as linear matrix inequalities (LMIs), which ensure asymptotic convergence of the estimation error. The effectiveness of the DUIO method is further validated in various scenarios through hardware-in-the-loop (HIL) implementation. This innovative approach significantly enhances the accuracy and reliability of SynRM state estimations and unknown input detections.

Optimising Damping Control in Renewable Energy Systems through Reinforcement Learning within Wide-Area Measurement Frameworks

2024-04-16T16:40:37+03:00

This paper introduces a reinforcement learning-based controller, utilising the deep deterministic policy gradient (DDPG) method, to mitigate low-frequency disturbances in electrical grids with renewable energy sources. It features a novel reward function inversely related to the control error and employs a state vector comprising absolute and integral errors to enhance error reduction. The controller, tested on a dual-region system with solar power, utilises phasor measurement unit (PMU) data for global inputs. Its performance is validated through time-domain simulations, pole-zero mapping, modal analysis, frequency response, and participation factor mapping, using a custom MATLAB and Simulink toolkit. The design accounts for communication delays and adapts to variable conditions, which proves to be effective in reducing oscillations and improving system stability.

Title

2024-07-11T14:25:38+03:00

A Practical Prediction Model for Surface Deformation of Open-Pit Mine Slopes Based on Artificial Intelligence

2024-05-28T15:55:53+03:00

To solve the problems of large prediction error, slow convergence speed, and poor generalisation ability of traditional models in predicting surface deformation of open-pit mine slopes, this paper proposes a new intelligent prediction model based on the Mayfly algorithm-optimised support vector machine (MA-SVM). In this method, the MA is used to optimise the SVM parameters to reduce the uncertainty of the model and avoid time-consuming parameter adjustment. To evaluate the proposed prediction model, real-world deformation data of the north slope of the Anjialing open-pit mine in Pingshuo city, China, are collected using the microdeformation monitoring radar and used to investigate the deformation prediction performance of the proposed method. The results of the analysis demonstrate that the proposed method is able to accurately predict the deformation of the surface of the mine slope and outperforms three existing popular methods, including SVM, genetic algorithm (GA)-SVM, and particle swarm optimisation (PSO)-SVM). The mean absolute error (MAE) of the proposed MA⁃SVM is 2.52 % while 6.56 %, 4.95 %, and 5.16 % for the SVM, GA-SVM, and PSO-SVM, respectively; the root mean square error (RMSE) of the proposed MA⁃SVM is 10.21 % while 30.79 %, 17.38 %, and 22.54 % for the other three methods. Because the proposed MA⁃SVM model is able to predict slope deformation using actual monitoring data, it is of practical importance in real-world applications for early warning on landslides of mine slopes.

Editorial Board

2024-07-11T14:29:13+03:00

Endocrine CNN-Based Fault Detection for DC Motors

2024-03-22T13:34:45+02:00

This paper presents a novel method for detecting and classifying faults in dynamic control systems empowered with DC motors, operating under laboratory conditions. The approach employs a convolutional neural network model enhanced with an artificial endocrine influence to evaluate the condition of the rotating motor shaft by analysing information from the vibration sensors mounted on the shaft itself. The trained network effectively classifies the level of unbalance in the system into three categories based on the vibrations: optimal (no unbalance), first and second degree of unbalance. To validate the efficiency of the proposed model, its performance was compared with the performance of deep learning algorithms commonly recommended for time-series classification: default convolutional neural network, fully convolutional neural network, and residual network. The new model was shown to perform classification tasks with the highest accuracy, proving to be an efficient fault diagnosis tool with a viable potential to be applicable in industrial predictive maintenance processes.

Position Control for Automatic Assembly Equipment Using a New Hybrid Fuzzy Controller

2024-05-04T06:07:49+03:00

Automatic assembly equipment is the key to improving the efficiency and quality of workpiece assembly. The precision of assembly directly influences the overall quality of the assembled product. To optimise the position control accuracy in the automatic assembly equipment, a variable universe fuzzy proportional integral (VUFPI) controller optimised by the sparrow search algorithm (SSA) is developed in this paper. The developed controller adopts the SSA to adjust in real time the universe of the fuzzy controller according to the deviation of the servo system. The servo system model is established to evaluate the performance of the proposed SSA-VUFPI controller; furthermore, the SSA-VUFPI controller is implemented in the automatic assembly equipment for experimental evaluation. The analysis results demonstrate that the proposed SSA-VUFPI controller is capable of improving the anti-interference ability and position accuracy of the servo system compared to traditional PI, VUFPI, and currently used back propagation neural network proportional-integral-derivative (BP-PID), fractional-order PID (FOPID), and SSA-PID controllers. Moreover, it effectively improves the position accuracy of the workpiece and ultimately improves the quality of the assembly.

Adaptive Traffic Management Model for Signalised Intersections

2024-04-08T12:17:10+03:00

As population increases, one of the factors affecting life is traffic. Efficient traffic management has a direct positive impact on issues such as time, carbon dioxide emissions, and fuel consumption. Today, an important parameter under the heading of traffic is the signalling systems for intersections, which are operated with fixed-time, semi-actuated, fully actuated, and fully adaptive control methods. In this study, an adaptive traffic management model is developed for signalised intersections. The adaptive traffic management model developed includes phase extension with minimum and maximum time intervals dependent on density and phase skip features. Additionally, the most distinctive feature of the model is its flexible phase structure rather than a sequential phase. The Heybe intersection, located within the boundaries of Antalya province, is modelled one-to-one in the simulation of urban mobility (SUMO) simulation programme with real intersection data. The developed adaptive traffic management model is applied to the Heybe intersection, and the effects of the model are revealed. Improvements obtained from the SUMO simulation programme were verified through visual inspection, and high-accuracy results were determined. As a result of the studies, it was found that the application of the adaptive traffic management model developed at Heybe intersection, which has approximately 50,000 vehicles passing daily, resulted in a 27.2 % improvement in the average delay per vehicle parameter, a 32.4 % improvement in the average waiting time per vehicle parameter, and a 16.7 % improvement in the average speed per vehicle parameter.

A Novel Framework for Digital Image Watermarking Based on Neural Network

2024-04-04T08:31:29+03:00

There are many instances of intellectual property rights violations due to the common usage of digital data on the Internet, including unauthorised use, copying, and theft of digital content. Intellectual property rights of digital photos must be upheld, as they are very valuable materials. Digital watermarking is a more modern method to do this. By using a watermark (WM), the owner's information is included into the content, which may then be shared or saved. When required, this technology will retrieve the encoded WM information to prove ownership. Different technologies have been investigated and created on the basis of existing technologies, fields of use, etc. This paper proposes a novel approach to digital watermarking based on a neural network. First, the trigger data set and noise data set are generated from the binary encoding and random cutting of the original training samples. Then, the pattern with higher watermark trigger accuracy is obtained from the trigger set. Simulation results show that the proposed algorithm performs better in terms of accuracy and computing time cost compared to existing algorithms

Comparative Assessment of P&O, PSO Sliding Mode, and PSO-ANFIS Controller MPPT for Microgrid Dynamics

2024-04-04T10:13:53+03:00

This paper compares different maximum power point tracking (MPPT) control strategies in microgrid dynamics, focussing on perturb and observe (P&O), adaptive neuro-fuzzy inference system (ANFIS), particle swarm optimisation (PSO), and PSO sliding mode controller techniques. The study investigates their performance under varying microgrid conditions, considering factors like weather and load variations. The simulation results provide a detailed comparative analysis of the power at the point of common coupling (PCC) for MPPT techniques at different time intervals. Both the P&O and PSO sliding mode recorded a power output of 287 kW, while PSO-ANFIS achieved a slightly higher power output of 294 kW. At 2.5 seconds, the P&O method recorded a power output of 712 kW, while the PSO sliding mode and the PSO-ANFIS techniques achieved 717 kW and 738 kW, respectively. Overall, the PSO-ANFIS technique consistently outperformed the other methods in terms of power output, demonstrating its effectiveness in maximising energy extraction and adaptability to dynamic conditions. These findings provide valuable insights for designing and implementing MPPT controllers in microgrid systems, emphasising the efficiency of the hybrid PSO-ANFIS technique in enhancing the overall performance and stability of renewable energy systems.

A Two-Tier Comparison Study of Three MPPT Control Algorithms for a PV-Powered Smart Greenhouse

2024-04-03T21:45:33+03:00

In this research work, three maximum power point tracking (MPPT) control algorithms based on power feedback are applied to two types of converters, namely Zeta and buck-boost, in the photovoltaic (PV) system. The PV system is intended to guarantee the power supply to a remote smart greenhouse. The control algorithms investigated are perturb and observe (P&O), incremental conductance (IncCond), and fuzzy logic (FLC) methods. Their performance are investigated and compared for each converter. It is found that the maximum power is always achieved, even during abrupt changes in irradiation or/and in temperature. The three methods have shown to have good performance; fast response time and very low steady-state error, with minor preference of the P&O method where the output voltage followed the input with high efficiency. The comparative study revealed that the power response time of the PV generator under stable conditions (constant irradiance and constant temperature) for P&O and IncCond was longer in the buck-boost converter than in the Zeta. On the other hand, the ripple level was better for the buck-boost. For the FLC, the maximum power was reached in a shorter time (short response time) with the smallest ripple. As for operation under variable environmental conditions, the Zeta outperformed the buck-boost for each control technique.