Measurement, Control, and Automation

Lifetime Estimation for Electrical Components in Distribution Systems Using Their Inspection and Maintenance Records

2021-02-27T03:15:42+00:00

Distribution system operators inspect electrical components in the distribution systems to maintain the reliability of power supply. Results of the inspection and necessary actions for them have been accumulating in databases as the inspection and the maintenance records. The authors propose an estimation method of operating lifetime of newly constructed electrical components as an application of these records. The aim of this study is to utilize the inspection and the maintenance records to decision making in expansion planning of the distribution systems. In the proposed method, decision tree learning is applied to analyze relationships between initial information of the electrical components (sets of specifications and weather conditions) and their operating lifetime. The resulting tree-like model uses initial information of the target component as input and estimates its operating lifetime. Numerical simulations are carried out for verifying the validity of the authors’ proposal using actual inspection and maintenance records accumulated in a Japanese electric power company. In addition, influential factors on the operating lifetime are specified through the numerical simulations and discussions on their results.

Short-Term Load Forecasting Using Long Short-Term Memory Based on EVN NLDC Data

2021-03-18T07:57:16+00:00

Load forecasting has always been a crucial part of an efficient power system planning an operation. Since the global electricity market has
developed rapidly in recent years, a load sequence has gradually become non-stationary and hence makes accurate forecasting difficult.
Nowadays, many techniques can be used for load forecasting such as fuzzy logic, similarday approach, expert systems, etc.. However, these
methods normally obtain undesired results due to considerable variation of electricity load. In this paper, a short-term load forecasting model is
proposed based on Long Short-Term Memory (LSTM) network combining linear regression algorithm and EVN-NLDC (National Load
Dispatch Centre) data. Short-term load forecasting has an enormous impact on unit commitment, strategic power reserve for national power
system and enhances the reliability of the power supply. National load data could be considered as a time series sequence consisting of two
components which are trend and residual. Linear regression (LR) is adopted for trend forecasting and LSTM is used to residual forecasting. In
addition, to evaluate the performance of the proposed model, artificial neural network (ANN) is utilized as a benchmark model. The results
show that the proposed model achieves the Mean Absolute Percentage Error (MAPE) ranged from 1% to around 4% for one-day ahead load
forecasting, whereas ANN model obtains over 4%.

Evaluate the performance of the hybrid magnetic bearing with the auxiliary clearance and propose an improvement

2021-04-09T04:27:23+00:00

Recently, Hybrid Magnetic Bearings (HBMs) are being widely used in many devices because they have advantages of both active and passive magnetic bearings. However, the way to combine effectively between permanent magnets and electromagnets always attracts many scientists. This paper focuses on the performance of a four-pole hybrid magnetic bearing with auxiliary clearance and suggests a new innovative way of using the magnetic gap. To evaluate the performance of these types of magnetic bearings, finite element analysis method (FEM) using ANSYS was applied. In addition, the simulation results are also compared with the calculated results by the equivalent magnetic circuit method (EMCM) to verify and give ideas to optimize the HMB. The evaluated results show outstanding advantages of the HBM using the magnetic gap compared to the other of using the auxiliary clearance.

Photovoltaic Power Generation Forecasting Utilizing Long Short Term Memory

2021-04-12T07:26:32+00:00

The modernization of the world has considerably reduced the prime sources of energy, such as coal, diesel and gas. Thus, alternative energy
sources based on renewable energy have been the main concentration nowadays to address the world’s energy demand and at the same time to
restrict global warming. Among these renewable energies, solar energy is the main source used to generate electricity through photovoltaic
(PV) systems. However, the output of PV power is highly intermittent. Thus accurately forecasting the output of PV systems is an important
requirement to ensure the stability and reliability of the grid. This study develops and validates a short-term PV power forecasting model
by using the combination of a genetic algorithm (GA) and Long Short Term Memory (LSTM). The performance of the proposed model is
compared with LSTM baseline model by three errors (Root mean square error(RMSE), Mean absolute error (MAE), and Mean absolute
percentage error (MAPE)) in two case studies.

Analysis and Comparison of the 4-pole 2DOF-HMBs

2021-03-11T06:44:11+00:00

Recently Hybrid Magnetic Bearings (HBMs) are being studied by many researchers because they have the ability to combine the advantages of the both active magnetic bearings and passive magnetic bearings. This paper focuses on two structures of the four-pole hybrid-type magnetic bearing, the ones with the auxiliary clearance and the others without the auxiliary clearance. In which the finite element analysis method (FEM) using ANSYS was applied to analyze and evaluate the performance of the above hybrid active magnetic bearings. The simulation results are also compared with the equivalent magnetic circuit method (EMCM) to verify and give ideas to optimize the design to improve the quality of the HMB.

Upper-limb movement analysis – A comparison between two OpenSim models

2021-03-05T02:25:18+00:00

In-vivo internal structural loads and muscle activation are complex to be performed, but these are crucial for quantitative evaluation of human movements. Hence some musculoskeletal models have been developed in the last decade which provide non-invasive estimations the body movements in normal conditions. Abnormal movements following injuries were simulated using biomechanical model in some previous studies. This study analysed upper limb movements by using a developed upper-extremity OpenSim model which includes strain-adjustable ligaments of the acromio-clavicular joint. In addition, movements analyzing results from using an available model developed from OpenSim community was used for comparison. The upper limb movements used for this research were recorded from two healthy participants. Shoulder range of motion resulted from the developed model and the available model are compared. The results from this comparison suggest that the proposed OpenSim model can be used for upper-limb motion analysis and evaluation.

Combination of ADRC and Input Shaping and Its Application for Vibration Suppression control of Flexible Link Robot

2021-03-03T15:21:59+00:00

Nhờ các ưu điểm như dễ chỉnh định, cho đáp ứng nhanh và tính bền vững khi tham số quá trình thay đổi, bộ điều khiển loại bỏ nhiễu chủ động (ADRC) đang được quan tâm nghiên cứu để thay thế cho bộ điều khiển PID truyền thống. Tuy nhiên, đối với nhiều hệ linh hoạt, bộ điều khiển ADRC không có khả năng làm suy giảm dao động dư. Phương pháp tạo dạng tín hiệu đầu vào là một phương pháp điều khiển tiền định được sử dụng rộng rãi cho mục đích chống rung. Bài báo này đề xuất kết hợp bộ điều khiển ADRC và và bộ điều khiển tạo dạng tín hiệu đầu vào để điều khiển chính xác vị trí cho tay máy linh hoạt một bậc tự do đồng thời suy giảm nhanh dao động dư của tay máy. Các kết quả mô phỏng cho thấy hiệu quả của phương pháp được đề xuất.

Modeling and Control of Wind-Solar-Battery Energy System -- Energetic Macroscopic Representation Approach

2021-04-09T04:38:28+00:00

Alternative energy sources, especially solar and wind energy play an important role in the near future. However, building systems to make full use of them in areas without grids is still a problem that attracts many studies. A commonly used system configuration is solar-wind energy system combined with a battery system. The modeling and control design of this mixed system by conventional methods is complex so that this paper brings an alternative approach more friendly to develop control and strategy using Energetic Macroscopic Representation for a renewable energy conversion system model including solar and wind power combined with a battery system. Thanks to this formalism tool, the control structure for this complex system is straightforward. Besides, the maximum power point tracking strategy is applied to maximize the energy received from nature. The operation of this power system to isolated loads is simulated in MATLAB/Simulink under various climate conditions, which shows that this system always satisfies the loads power demand with the stably voltage.

A Novel Multi-Level Inverter for Renewable Energy System Applications

2021-05-01T04:39:47+00:00

This article proposes a new multi-level inverter using switch-capacitor structure which works based on switching capacitor on series/parallel in the circuit through the switches. This configuration reduces the number of components include semiconductor switches, diodes and input source than the traditional inverters, thereby reducing the cost and the size of the system. The proposed inverter uses two capacitors, two diodes and nine switches to create a nine-levels voltage output. In this paper, the simulation results are implemented by PSIM 9.0 software and the experimental results are shown.