Due to the increment in hand motion types, electromyography (EMG) features are increasingly required for accurate EMG signals classification. However, increasing in the number of EMG features not only degrades classification performance, but also increases the complexity of the classifier. Feature selection is an effective process for eliminating redundant and irrelevant features. In this paper, we propose a new personal best (Pbest) guide binary particle swarm optimization (PBPSO) to solve the feature selection problem for EMG signal classification. First, the discrete wavelet transform (DWT) decomposes the signal into multiresolution coefficients. The features are then extracted from each coefficient to form the feature vector. After which pbest-guide binary particle swarm optimization (PBPSO) is used to evaluate the most informative features from the original feature set. In order to measure the effectiveness of PBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA), modified binary tree growth algorithm (MBTGA), and binary differential evolution (BDE) were used for performance comparison. Our experimental results show the superiority of PBPSO over other methods, especially in feature reduction; where it can reduce more than 90% of features while keeping a very high classification accuracy. Hence, PBPSO is more appropriate for application in clinical and rehabilitation applications.

Features extracted from the electromyography (EMG) signal normally consist of irrelevant and redundant features. Conventionally, feature selection is an effective way to evaluate the most informative features, which contributes to performance enhancement and feature reduction. Therefore, this article proposes a new competitive binary grey wolf optimizer (CBGWO) to solve the feature selection problem in EMG signals classification. Initially, short-time Fourier transform (STFT) transforms the EMG signal into time-frequency representation. Ten time-frequency features are extracted from the STFT coefficient. Then, the proposed method is used to evaluate the optimal feature subset from the original feature set. To evaluate the effectiveness of proposed method, CBGWO is compared with binary grey wolf optimization (BGWO1 and BGWO2), binary particle swarm optimization (BPSO), and genetic algorithm (GA). The experimental results show the superiority of CBGWO not only in classification performance, but also feature reduction. In addition, CBGWO has a very low computational cost, which is more suitable for real world application.

The recent proliferation of residential solar photovoltaic systems has prompted several technical challenges to the operation of low voltage (LV) distribution networks. More specifically, the mismatch of the solar generation and demand profiles, particularly during the midday when the demand is low and solar generation is high, can lead to network overvoltages and increased network losses. In addition, the solar photovoltaic system is not able to reduce the system’s maximum demand, given the residential LV network would normally have an evening peak when the sun goes down. In this regard, this paper examines two different control strategies in designing the battery energy storage system. One aims to eliminate reverse flow caused by the surplus solar energy and the other aims for peak demand reduction.

The usage of battery energy storage system (BESS) can be a significant technology to improve the performance of power systems. Optimal sizing of BESS can reduce power losses, improve voltage profile and relieve peak demand in power systems. This paper aims to establish a simulation-based optimization in DIgSILENT Programming Language (DPL) script in DIgSILENT PowerFactory environment, which is more efficient for data exchange between operations. Problem for optimization of BESS sizing and placement are formulated in DPL script with analytic algorithm and particle swarm optimization (PSO) with loadflow simulation. The IEEE 9-bus system is the test case used to demonstrate and discuss the application of algorithms in DPL script. The placement of BESS is identified to be optimal with lowest power losses of 4.962 MW at bus 5 and the optimal BESS size of 47.168 MW with operation cost of 4852.65 $/h are determined. The output of the study concludes that optimal sizing of BESS can be applied in solving power system problems effectively with DPL operations. This paper serves as reference for researchers to study and implement optimization in DIgSILENT DPL script.

Modelling and Sizing a Grid-connected PV-Battery System Using DIgSILENT for Powering UTeM Main Campus