ANFIS-based droop control of an AC microgrid system: considering intake of water treatment plant

Provision of an efficient water supply system (WSS) is one of the top priorities of all municipals to ascertain adequate water supply to the city. Intake is the lifeline of the water supply system and largely effects the overall plant efficiency. The required power supply is generally fed from the main grid, and a diesel generator is commonly used as a power backup source. This results in high pumping cost as well as high operational cost. Moreover, due to operation of motor pumps and other auxiliary loads, frequent maintenance is required. Therefore, to avoid various challenges and to efficiently operate the intake system, microgrid concept has been introduced in this paper. Various distributed generations (DGs) such as solar photovoltaic (PV), interior permanent magnet machine (IPM) wind turbine generator and Battery energy storage system (BESS) are incorporated in the microgrid system. Additionally, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) is proposed, where P-f and Q-V droop is considered while training the ANFIS data; after successful training, the microgrid voltage and frequency are controlled as per system requirement. Simulation of the microgrid system shows good results and comparison with the generalized droop control (GDC) method is done using MATLAB/Simulink software

Simultaneous analysis of frequency and voltage control of the interconnected hybrid power system in presence of FACTS devices and demand response scheme

This work confers the simultaneous analysis of voltage and frequency control of the 3-area interconnected hybrid power system (IHPS) consisting of parabolic-trough solar power system (PSP), wind power system (WPS) and dish-stirling solar power system (DSP) under the paradigm of microgrid. The speculated result of the IHPS is presented and analyzed considering real and reactive power as the function of both voltage and frequency. 9The proposed IHPS under investigation has been mathematically modeled for direct coupling like active power-frequency and reactive power-voltage relationships and cross coupling like active power-voltage and reactive power-frequency? relationships. The system responses under different operating conditions have been investigated to see the cross-coupling behavior of the proposed IHPS in the presence of voltage compensating devices like dynamic voltage restorer (DVR) and Static Synchronous Compensator (STATCOM). Further, Demand Response Scheme (DRS) as a frequency control strategy has been considered to enhance the system stability. System responses have been critically analyzed under Mine Blast Algorithm (MBA) based proportional-integral-derivative (PID) controllersThis work was made possible by NPRP grant # [ 13S-0108-20008 ] from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors".Scopu

Voltage stability of solar dish-Stirling based autonomous DC microgrid using grey wolf optimised FOPID-controller

This paper proposes the maiden application of a modern meta-heuristically optimised fractional order PID (FOPID) controller for ensuring the voltage stability of a DC microgrid system having PMDC generator coupled to a solar dish-Stirling engine. A significant cost reduction and efficiency enhancement can be achieved by the proposed topology. Popular meta-heuristic optimisation algorithms such as grey wolf optimiser (GWO), mine blast algorithm (MBA) and particle swarm optimisation (PSO) are adopted for tuning the gain constants of the controllers. The major focus of this article is to keep the common DC bus voltage of the microgrid at the desired steady-state value under varying load and varying solar insolation conditions. The results demonstrate the superior performance of the proposed controller

Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-Heuristically Optimized Non-Local Means Filter

Electroencephalogram (EEG) signals may get easily contaminated by muscle artifacts, which may lead to wrong interpretation in the brain–computer interface (BCI) system as well as in various medical diagnoses. The main objective of this paper is to remove muscle artifacts without distorting the information contained in the EEG. A novel multi-stage EEG denoising method is proposed for the first time in which wavelet packet decomposition (WPD) is combined with a modified non-local means (NLM) algorithm. At first, the artifact EEG signal is identified through a pre-trained classifier. Next, the identified EEG signal is decomposed into wavelet coefficients and corrected through a modified NLM filter. Finally, the artifact-free EEG is reconstructed from corrected wavelet coefficients through inverse WPD. To optimize the filter parameters, two meta-heuristic algorithms are used in this paper for the first time. The proposed system is first validated on simulated EEG data and then tested on real EEG data. The proposed approach achieved average mutual information (MI) as 2.9684 ± 0.7045 on real EEG data. The result reveals that the proposed system outperforms recently developed denoising techniques with higher average MI, which indicates that the proposed approach is better in terms of quality of reconstruction and is fully automatic

SCADA based intake monitoring for improving energy management plan: Case study

Intake is one of the most important pumping stations in water treatment plant (WTP) and greatly effects the overall plant efficiency. Intake mainly consist of high-tension (HT) motor pump such as vertical turbine pump and submersible pump. The required power supply for intake is generally fed from the main grid through distribution company/department. At the same time, high operational cost of the diesel generator (DG) power back up source results in a huge burden. Also, internet of things (IoT) and Supervisory control and data acquisition (SCADA) based system is widely used nowadays. Therefore, study regarding monitoring strategies of intake is very important and is rarely to be found. In this paper, monitoring strategies of Greater Aizawl Water Supply Schemes phase II is studied. The main objectives are; to study and analyze pump energy consumption (viz. Feeder Current, Frequency etc.); to implement ladder logic for monitoring energy parameters using MOVEREAL. The study shows monitoring of intake energy parameters as well as the feeding substation, showing that SCADA can be efficiently used as an energy management plan