Electrical Disturbances Research Articles

A Comprehensive Review on Distributed Feeder Protection Problems in the Gaza Strip 22/0.4 kV Overhead Power Distribution System – A Case Study

This paper provides a perspective on protective system at 22kV overhead feeders in Gaza distribution network using symmetrical components method and ETAP software. Electrical disturbances from high rate of outage feeders at the 22 kV overhead power lines frequently cause disruptions on the generating plant and customer loads. One sub-transmission feeder from a parallel set of ten 22 KV feeders (140 MW) in the Gaza west substation is selected as a case study in this research. Because the protection system lacks a data fault recorder (DFR), the paper calculates the fault currents at both the nearest substation bus and the last bus in the distributed feeder using the symmetrical components method and verifies the results by using ETAP software simulation. The results from calculating the minimum fault at the last bus indicated that the current protective parameters are reasonable and satisfied for detecting and interrupting the occurring faults. This author paper proposes increasing the fault clearing time at farthest feeder bus to make protective functions less sensitive by adjusting TMS to 0.08 for time overcurrent and earth fault and setting pickup level to 3.8 for instantaneously overcurrent. The design model is run on the selected feeder, and simulation results guarantee the proposed model's accurate performance for both overcurrent and earth faults after changing the fault clearing time. There will be no miscoordination of protective devices or operational risks while running the design model for the selected feeder with new settings. Gaza's distribution feeder network requires structural improvements to become more reliable and to reduce the high rate of feeder trip, and practical recommendations to improve current installation distribution grid are included in this paper.

Ionospheric irregularities observed during the geomagnetic storm of March 2015 and June 2015 over Ethiopia

In this paper we studied the occurrence of ionospheric irregularities over Ethiopia as the consequence of the intense geomagnetic storm of March 2015 and June 2015. Total Electron Content (TEC) data obtained from six Ethiopian GPS reciever stations and those data were used to derive the ionospheric irregularities proxy indices, e.g.,rate of change of TEC (ROT) and ROT index (ROTI). These indices were used to characterize the intensity of the fluctuation of total electron content. ROTI was used as a criterion of ionospheric irregularities that took place during the storm. These indices were characterized along side with the disturbance storm time (Dst),the Interplanetary Electric Field (IEFy) and the SYM-H index (high resolution version of Dst) from ground-based magnetometers to see the effect of geomagnetic storm on horizontal component of geomagnetic field. Irregularities manifested in the form of fluctuations in TEC. Prompt penetration of electric field (PPEF) modulated the behaviour of irregularities during the initial and recovery phases of the geomagnetic storms. As a result,it was observed that the irregularities in the ionosphere of Ethiopia was in the range from weak to moderate. These irregularities generally occurred during the initial and recovery phases of the storm at all selected stations. The effect of electric field is found to depend on the local time at which the IMF Bz turned into southward. Therefore,east - west fluctuating Ey in March storm main phase is associated with suppression of irregularities in all stations and eastward Ey in the main phase of June 2015 is associated with small scale irregularity formation. The generation (inhibition) of irregularities is related to the effect of eastward (westward) storm time electric field disturbance dynamo electric fields and prompt penetration electric fields that created favourable (unfavourable) conditions for the generation of irregularities by uplifting (lowering) the F region.

Mitigating Electrical Disturbances with Hybrid Distribution Transformer

Hybrid transformers (HT) have the advantages of the conventional transformer, the regulatory abilities of power electronic converters, and reduce the impact of the grid. The impacts of the existing grid are voltage sag, voltage swell, harmonic distortion, and voltage unbalanced. The power electronic converter has a controllable advantage such as regulating the voltage and can transfer only a fraction of the power. The aim of the paper is to augment the conventional power distribution transformer with a partially rated power electronic module to enhance flexibility and introduce new features to the distribution transformer. In this paper, the proposed back-to-back converter included an active front rectifier and a modular multilevel converter (MMC) was simulated by MATLAB/Simulink software. The proposed back-to-back converter was used at the primary side of the distribution transformer to compensate for the voltage sag and swell issues. The simulation results were obtained under different conditions such as various supply voltages and various loads. Hence, the proposed system has the ability to regulate the output voltage under various conditions with ±10%.

Power Disturbance Monitoring through Techniques for Novelty Detection on Wind Power and Photovoltaic Generation.

Novelty detection is a statistical method that verifies new or unknown data, determines whether these data are inliers (within the norm) or outliers (outside the norm), and can be used, for example, in developing classification strategies in machine learning systems for industrial applications. To this end, two types of energy that have evolved over time are solar photovoltaic and wind power generation. Some organizations around the world have developed energy quality standards to avoid known electric disturbances; however, their detection is still a challenge. In this work, several techniques for novelty detection are implemented to detect different electric anomalies (disturbances), which are k-nearest neighbors, Gaussian mixture models, one-class support vector machines, self-organizing maps, stacked autoencoders, and isolation forests. These techniques are applied to signals from real power quality environments of renewable energy systems such as solar photovoltaic and wind power generation. The power disturbances that will be analyzed are considered in the standard IEEE-1159, such as sag, oscillatory transient, flicker, and a condition outside the standard attributed to meteorological conditions. The contribution of the work consists of the development of a methodology based on six techniques for novelty detection of power disturbances, under known and unknown conditions, over real signals in the power quality assessment. The merit of the methodology is a set of techniques that allow to obtain the best performance of each one under different conditions, which constitutes an important contribution to the renewable energy systems.

FPGA-based online voltage/current swell segmentation and measurement

Power-signal distortion is one of the main problems industry faces since it is produced by the use of non-linear loads and environmental disturbances. Swells are among the most common electrical disturbances that cause low power quality (PQ) in electric-energy delivery systems. Different methodologies have been proposed to detect disturbances, and as one of the most common electrical disruptions, the monitoring of voltage/current swells is an essential task for opportune correction and increase of the electric-system PQ. Unfortunately, most of the proposed methods for PQ monitoring do not perform a measurement of the disturbance time span. Hence, in this work, a novel method for detecting and measuring the time-length of voltage/current swells through spline interpolation and Otsu segmentation, implemented in an FPGA, is proposed. Obtained results from signals generated through a computer model and acquired from real experimentation demonstrate that the proposed approach can measure swell disturbances in the electric voltage/current signal from a distribution system with high resolution and precision, performing the time-span measurement of a swell disturbance in 81.3 μs, with high certainty of up to 99.945%. Furthermore, the portability of the proposed hardware processing unit is shown by its implementation in FPGA devices from two different vendors.

Multi-Instrumental Observations of Midlatitude Plasma Irregularities over Eastern Asia during a Moderate Magnetic Storm on 16 July 2003

This study presents the observations of midlatitude plasma irregularities over Eastern Asia during a moderate magnetic storm on 16 July 2003. Multi-instrumental observations, including the ground-based ionosondes, the GNSS networks, and the CHAMP and ROCSAT-1 satellites, were utilized to investigate the occurrence and characteristics of midlatitude plasma irregularities. The midlatitude strong spread F (SSF) mainly occurred in the midnight–morning sector as observed by ionosondes over Japan during this storm. SSF was related to plasma depletions, which is also recorded by GNSS network in the form of the enhancement of the rate of total electron content (TEC) change index (ROTI). The possible mechanism for the generation of SSF is that the enhanced eastward electric fields, associated with the prompt penetration electric fields and disturbance dynamo electric fields, cause the uplift and latitudinal extension of equatorial plasma bubbles (EPBs) to generate the observed midlatitude SSF further. Meanwhile, plasma density increased significantly under the influence of this storm. In addition, other common type of spread F, frequency spread F (FSF), was observed over Japan on the non-storm day and/or at high latitude station WK545, which seems to be closely related to the coupling of medium-scale traveling ionospheric disturbances (MSTIDs) and sporadic E (Es) layer. The above results indicate that various types of midlatitude spread F can be produced by different physical mechanisms. It is found that SSF can significantly affect the performance of radio wave propagation compared with FSF. Our results show that space weather events have a significant influence on the day-to-day variability of the occurrence and characteristics of ionospheric F-region irregularities at midlatitudes.

Statistical analysis of electric field perturbations in ELF based on the CSES observation data before the earthquake

To explore the correlation between earthquakes and the pre-earthquake ionospheric shallow frequency (ELF) electric field perturbations phenomenon, the paper investigated the pre-earthquake ionospheric perturbations phenomenon, and then the Spatio-temporal evolution characteristics of the electromagnetic field before and after the global Ms ≥6.0 strong earthquakes from 2019 to 2021 were statistically analyzed. In this paper, the power spectrum data of the ELF (19.5–250 Hz) band of ionospheric electric field observed by the China Seismo-Electromagnetic Satellite (CSES) electric field detector are preclinically processed by the C-value method. A stable background field observation model was constructed using the data from 75 to 45 days before the earthquake observed by the CSES in the range of 15° above the epicenter. Then, the amplitude of the spatial electric field disturbance over the epicenter relative to the background field is extracted. Finally, the superposition analysis method statistically analyzes the spatial and temporal evolution of the spatial electric field before and after the earthquake with different characteristics. The statistical results show that the anomalies first appear in the fourth period (15–19 days before the earthquake) and the third period (10–14 days before the earthquake) and then reach the most vital and most evident during the pro-earthquake period (4 days before the earthquake and the day of the earthquake); In terms of the intensity of the anomalies caused, the magnitude seven earthquakes are stronger than the magnitude 6.0–7.0 earthquakes, and marine earthquakes are stronger than land earthquakes; in terms of the ease of observing the anomalies, the magnitude 7.0 and above are more accessible to observe than the magnitude 6.0–7.0 earthquakes, and marine earthquakes are more accessible to observe than land earthquakes.

EMD based statistical analysis of nighttime pre-earthquake ULF electric field disturbances observed by CSES

To explore the correlation between earthquakes and spatial ultra-low frequency electric field disturbances and to study the phenomenon of seismic ionospheric disturbances, this study uses 3 years of electric field ULF band data from 2019 to 2021 observed by the electric field detector carried by the CSES to identify anomalous disturbances using the anomaly automatic detection algorithm based on empirical mode decomposition for the 2,329 seismic events of magnitude not less than 5.0 and the electric field ULF disturbances in this period are analyzed by Superposed Epoch Analysis, and the statistical results are compared and analyzed in depth by earthquake location and different magnitudes in terms of both spatial and temporal scales and spatial distribution. The results show that: 1) There is a correlation between earthquakes of magnitude not less than 5.0 and ultra-low frequency disturbances in the electric field. The abnormal disturbance mainly occurred 11 days before the earthquake, 2 days before the earthquake to the day of the earthquake, and the location of the earthquake is within 200 km from the epicenter. 2) Sea earthquakes can observe more pre-seismic anomalous electromagnetic disturbances than land earthquakes. 3) In terms of earthquake magnitude, the larger the magnitude, the earlier the pre-earthquake anomalous disturbances appear and the wider the range of anomalies. This study provides an effective way to explain seismic ionospheric phenomena, and also provides a reference for the application of electromagnetic monitoring satellites in earthquake prediction and early warning as well as disaster prevention and mitigation.

Quantum mechanical aspects of cardiac arrhythmias: A mathematical model and pathophysiological implications

<abstract> <p>Cardiac arrhythmias are serious myocardial electrical disturbances that affect the rate and rhythm of heartbeats. Despite the rapidly accumulating data about the pathophysiology and the treatment, new insights are required to improve the overall clinical outcome of patients with cardiac arrhythmias. Three major arrhythmogenic processes can contribute to the pathogenesis of cardiac arrhythmias; 1) enhanced automaticity, 2) afterdepolarization-triggered activity and 3) reentry circuits. The mathematical model of the quantum tunneling of ions is used to investigate these mechanisms from a quantum mechanical perspective. The mathematical model focuses on applying the principle of quantum tunneling to sodium and potassium ions. This implies that these ions have a non-zero probability of passing through the gate, which has an energy that is higher than the kinetic energy of ions. Our mathematical findings indicate that, under pathological conditions, which affect ion channels, the quantum tunneling of sodium and potassium ions is augmented. This augmentation creates a state of hyperexcitability that can explain the enhanced automaticity, after depolarizations that are associated with triggered activity and a reentry circuit. Our mathematical findings stipulate that the augmented and thermally assisted quantum tunneling of sodium and potassium ions can depolarize the membrane potential and trigger spontaneous action potentials, which may explain the automaticity and afterdepolarization. Furthermore, the quantum tunneling of potassium ions during an action potential can provide a new insight regarding the formation of a reentry circuit. Introducing these quantum mechanical aspects may improve our understanding of the pathophysiological mechanisms of cardiac arrhythmias and, thus, contribute to finding more effective anti-arrhythmic drugs.</p> </abstract>

Multi-Objective Feedback Nonlinear Sliding Mode Control Strategy Based Integrated Regulator for Hydropower Set With Sloping Ceiling Tailrace Tunnel

In this paper, a multi-objective feedback nonlinear sliding mode control strategy is designed to reduce the impact of the disturbance on the hydro-generator set in the tailrace tunnel with a sloping ceiling, aiming at ensuring the safe and stable operation of the hydro-generator set under severe electrical and hydraulic conditions. Firstly, according to the actual hydraulic structure of a hydropower station with a tailrace tunnel with a sloping ceiling, a comprehensive model of a nonlinear hydro-generator set with excitation control and guide vane opening control is established. Secondly, according to the design method of multiple objective feedback control law and the principle of sliding mode control, the paper establishes a standard matrix of Brunovsky foundation for accurate feedback linearization by selecting control targets of turbine flow, guide vane opening, active power, speed, and terminal voltage, designs the sliding mode control system, and finally obtains the general nonlinear control law through nonlinear transformation. Simulation results show that, compared with PID control and Multiple-index Nonlinear Control (MINC), the Multi-objective Feedback Nonlinear Sliding Mode Control (MOFNSMC) in this paper has a faster recovery capability and good disturbance-resistant capability with a small oscillation amplitude of the hydro-generator terminal potential in the case of electrical disturbance. In the case of hydraulic disturbance, it can effectively reduce the overshoot and the number of oscillations of the turbine waterhead and the flow and the water hammer pressure on the hydraulic structure, to improve the safe and stable operation of the hydro-generator set.

Pemanfaatan Simulasi Monte Carlo Untuk Perhitungan Kinerja Gangguan Tenaga Listrik Pada PT PLN (Persero) UIW Suluttenggo

Objective: This study aims to determine the optimum number of electrical disturbances at PLN UIW Suluttenggo both in terms of frequency and duration of disruption to determine the level of quality of electric power reliability service on the customer side. Quality reliability or "reliability" is related to the ability of a system to deliver electricity to all points of its user in the appropriate standards and quantities. There are two things related to the reliability of electric power (1) Adequacy (adequacy) and (2) System security (security).
 Method: Monte Carlo simulation is a simulation model that includes a series of random sampling with a probability distribution that can be known and determined, so this simulation can be used. In this study, data was taken from the number of electrical disturbances that occurred at PLN UIW Suluttenggo in 7 service unit branches using SAIDI and SAIFI data in 2021 and 2022.
 Findings: The finding of this study is that the use of Monte Carlo simulations can be used to analyze ganggaun data where the calculation results compared to the calculation of the excell formula produce almost the same results.
 Results: The results of the interference performance calculation showed the best performance were UP3 Manado (1.65), UP3 Kotamobagu (1.55) and UP3 Gorontalo (1.05). As for each interruption performance for the shortest duration of outage is UP3 Kotamobagu (0.75) and the smallest interference frequency is UP3 Manado (1.0).

Left atrial epicardial adipose tissue exacerbates electrical conduction disturbance in normal-weight patients undergoing pulmonary vein isolation for atrial fibrillation.

Epicardial adipose tissue (EAT) exacerbates both electrical and structural remodeling in obese atrial fibrillation (AF) patients, but the impacts of EAT on atrial arrhythmogenicity remain unclear in normal-weight AF patients. Therefore, we sought to investigate this issue using electroanatomic mapping. We enrolled drug-refractory 105 paroxysmal AF patients in the normal body mass index range (18.5-24.9 kg/m2 ), who had undergone electroanatomic mapping after pulmonary vein isolation (PVI). One day before PVI, we assessed P-wave duration in a 12-lead electrocardiogram and left atrial (LA)-EAT volumes using contrast-enhanced computed tomography. The patients were divided into two groups based on the median LA-EAT volume (16.0 ml); the high LA-EAT group (≥16.0 ml, n = 53) and low LA-EAT group (<16.0 ml, n = 52). We compared P-wave duration, LA conduction velocity and bipolar voltage, the presence of low-voltage zone (<0.5 mV), and LA volume index on echocardiography between the two groups. The LA bipolar voltage, low-voltage zone, and LA volume index were not different between the high and low LA-EAT groups. However, P-wave duration was significantly longer in the high group than in the low group (p < .001). Additionally, the LA conduction velocity was significantly more depressed in the high group than in the low group (p < .001). Multivariate linear regression analysis revealed that LA-EAT volume was correlated with P-wave duration (β = .367, p < .001) and conduction velocity (β = -.566, p < .001), respectively. Increased LA-EAT volumes were associated with electrical conduction disturbance after PVI in normal-weight patients with AF. P-wave duration may be a clinically useful predictor of LA-EAT.

The Impact of Integration of the VSC-HVDC Connected Offshore Wind Farm on Torsional Vibrations of Steam Turbine Generators

For remote offshore wind farms, transmitting power to the main onshore grid via a Voltage Source Converter High Voltage Direct Current (VSC-HVDC) system is the mainstream of power transmission. It is not only cost-effective in long-distance transmission, but also can fully meet the grid side requirements such as black start, voltage support, fault ride through and frequency support. However, it still has some problems, such as the possible impact on the power grid needing to be paid attention to. In this paper, its impact on the torsional responses of turbine generator units neighboring to the onshore side of AC bus is studied by using the DIgSILENT PowerFactory software. It is found that the effects of the Sub-Synchronous Torsional Interaction (SSTI) with onshore controls and the generator de-rating operations can significantly affect the damping ratio of turbine torsional modes, whereas the effects of the machine configurations and the amount of wind farm power integrated can affect the electrical torque disturbance. The most noteworthy is that their effects can be superimposed on each other if these factors act simultaneously, which would lead to increased vibrations and reduce the turbine shaft’ s life. The findings will be helpful for avoiding accidents caused by torsional vibrations when it is going to integrate a VSC-HVDC connected wind farm into a power grid.

In Silico Modelling to Assess the Electrical and Thermal Disturbance Provoked by a Metal Intracoronary Stent during Epicardial Pulsed Electric Field Ablation.

Background: Pulsed Electric Field (PEF) ablation has been recently proposed to ablate cardiac ganglionic plexi (GP) aimed to treat atrial fibrillation. The effect of metal intracoronary stents in the vicinity of the ablation electrode has not been yet assessed. Methods: A 2D numerical model was developed accounting for the different tissues involved in PEF ablation with an irrigated ablation device. A coronary artery (with and without a metal intracoronary stent) was considered near the ablation source (0.25 and 1 mm separation). The 1000 V/cm threshold was used to estimate the ‘PEF-zone’. Results: The presence of the coronary artery (with or without stent) distorts the E-field distribution, creating hot spots (higher E-field values) in the front and rear of the artery, and cold spots (lower E-field values) on the sides of the artery. The value of the E-field inside the coronary artery is very low (~200 V/cm), and almost zero with a metal stent. Despite this distortion, the PEF-zone contour is almost identical with and without artery/stent, remaining almost completely confined within the fat layer in any case. The mentioned hot spots of E-field translate into a moderate temperature increase (<48 °C) in the area between the artery and electrode. These thermal side effects are similar for pulse intervals of 10 and 100 μs. Conclusions: The presence of a metal intracoronary stent near the ablation device during PEF ablation simply ‘amplifies’ the E-field distortion already caused by the presence of the vessel. This distortion may involve moderate heating (<48 °C) in the tissue between the artery and ablation electrode without associated thermal damage.

Studi Kelayakan Sistem Pentanahan pada Gedung Teknik Elektro Universitas Andalas

The grounding system is one part of the electric power system that has an important role in flowing overcurrent from the electric power system to the ground due to electrical power system disturbances or lightning strikes that can endanger the building and its surroundings. This grounding system aims to protect humans, electrical equipment, and electrical installations from the danger of electrical short circuits. In compliance with official regulations, safety standards and according to general electrical installation regulatory standards (PUIL) to avoid the danger of lightning strikes in buildings, it is necessary to have grounding resistance value of less than five ohms and is made as small as possible. In this study, the measurement of grounding resistance seen from the depth of the grounding and the grounding resistance are arranged in parallel. The results of this study indicate that the parallel grounding resistance method produces a more significant reduction in the value of grounding resistance compared to the grounding resistance in a certain depth From the calculation results, the resistance value of the Electrical Engineering Building at Andalas University using a rod electrode with a diameter of 0.016 meters is 24.5 Ω with an electrode depth of 0.5 meters and at a depth of 1 meter a grounding value of 17.52 Ω is obtained. The deeper the depth of the electrode, the smaller the resulting resistance value, and installing a grounding system with the multirod method, namely arranging electrodes in parallel with a certain depth, obtains less resistance compared to not parallelizing the electrodes

Recurrent Convolutional Neural Network-Based Assessment of Power System Transient Stability and Short-Term Voltage Stability

Transient stability (TS) and short-term voltage stability (STVS) assessment are of fundamental importance for the operation security of power systems. Both phenomena can be mutually influenced in weak power systems due to the proliferation of power electronic interface devices and the phase-out of conventional heavy machines (e.g., thermal power plants). There is little research on the assessment of both types of stability together, despite the fact that they develop over the same short-term period, and that they can have a major influence on the overall transient performance driven by large electrical disturbances (e.g., short circuits). This work addresses this open research challenge by proposing a methodology for the joint assessment of TS and STVS. The methodology aims at estimating the resulting short-term stability state (STSS) in stable, or unstable conditions, following critical events, such as the synchronism loss of synchronous generators (SG) or the stalling of induction motors (IM). The estimations capture the mechanisms responsible for the degradations of TS and STVS, respectively. The paper overviews the off-line design of the data-driven STSS classification methodology, which supports the design and training of a hybrid deep neural network RCNN (recurrent convolutional neural network). The RCNN can automatically capture spatial and temporal features from the power system through a time series of selected physical variables, which results in a high estimation degree for STSS in real-time applications. The methodology is tested on the New England 39-bus system, where the results demonstrate the superiority of the proposed methodology over other traditional and deep learning-based methodologies. For reference purposes, the numerical tests also illustrate the classification performance in special situations, when the training is performed by exclusively using measurements from generation and motor load buses, which constitute locations where the investigated stability can be observed.

Pengujian Tahanan Isolasi Pada Transformator Distribusi 160 kVA Di PT. PLN (PERSERO) UP3 Bengkulu

The power transformer is a high voltage equipment that functions as a distributor of electric power from high voltage to low voltage or from low voltage to high voltage. If the transformer's isolation condition deteriorates, it can have an impact on operating failure and the most fatal is that the transformer is damaged which can cause electrical system disturbances. To overcome this it is necessary to test the insulation resistance. This test is carried out using the polarization index method to determine the feasibility of the insulation resistance in the transformer. The use of this method is the most accurate and effective way to determine the feasibility of insulation resistance in transformers at distribution substations in Indonesia. This study took data at the location of PT. PLN (Persero) UP3 Bengkulu. In the insulation resistance test, the polarity index measurement results are obtained. Based on the data that has been collected then proceed with calculations and analysis to obtain better results. The ground-primary polarization index value shows that it is in good condition with a primary-ground value of 1.25 and a secondary-ground polarization index value of 1.5 (good standard between 1.25 – 2.0).

Generation of Power Spectrum of Power Quality Disturbances using MATLAB

The aim of this paper is to present power spectrum of electrical power quality disturbances obtained by mathematical modeling. Signals considered for analysis are transients, harmonics, interruption, voltage sag, voltage swell, voltage fluctuations and voltage flicker. These power quality disturbances affect the continuity of supply and results in reduction of stability of the power system. So there is a necessity for an analysis of power quality disturbances in different perspectives. Power spectrum representation of the different disturbance signals is obtained. The values of power in Decibels and frequency in Hertz are obtained which gives more information about the signals in time domain. Power spectrum gives useful information about the variations in signals. Waveforms for transients, interruption, sag, swell, harmonics, voltage fluctuations, and voltage flicker are generated using mathematical equations and their corresponding power spectrum representations are obtained in MATLAB environment.

An active primary frequency regulation strategy for grid integrated wind farms based on model predictive control

The increasing penetration level of wind power can reduce the dependency on fossil fuels, but it is accompanied with challenges such as the jeopardized dynamic stability of the frequency of power grid. As an effective method to improve frequency dynamic stability, primary frequency regulation (PFR) is conventionally based on the feedback of measured frequency, by which means it is conducted through analyzing historical information. By taking the predictive information into account, this paper introduces an active PFR (APFR) strategy for grid integrated wind farms (WFs) to enhance control performance of PFR. According to small-signal models of power system integrated with WFs based on conventional PFR and APFR, the state-space predictive models are established to obtain the predictive states. Then, to take active measures to deal with the electrical load disturbance, the predicted frequency is optimized in a receding horizon period by adjusting the PFR power reference of WFs flexibly. Moreover, a finite terminal weighting matrix based Lyapunov​ function is presented to prove the asymptotic stability of the closed-loop model predictive control system. Finally, extensive case studies based on the standard test systems are performed to validate the effectiveness of the theoretical analysis and the superiority of APFR strategy.

Identifying Epileptic Seizure by Optimized Feature Extraction Process Using the Method of Feature Fusion Technique

Epilepsy is a brain disorder that results in seizures; in general seizure is a suddenly occurring uncontrollable electrical disturbance in the brain. These disturbances in the brain can lead to changes in behaviour, feelings, movements, etc. It is highly essential for the patients suffering with epilepsy to be diagnosed and treated. The normal detection of epilepsy is done using EEG signals which are time consuming. This paper aims at proposing a methodology to diagnose epilepsy by the use of EEG signals by establishing a correlation between statistical calculations and EEG signals. A various set of features are applied to the epilepsy and non-epilepsy dataset. Features such as time domain frequency which include mean, skewness, variance, kurtosis, standard deviation, approximate entropy, zero crossings, power spectrum and frequency domain features that include signal energy and total signal area, average DWT coefficient, signal relation features and human brain graph features. Further, considering these features, feature fusion and optimization aka FFO is carried out which helps in analysing the features in an optimal way for further classification. Moreover, feature fusion and its optimization helps in exploring the new features that helps in enhancing the distinguish between classes. These features help diagnosis of the brain disorder in a very time efficient manner with higher accuracy. In this paper, we propose a feature fusion methodology for the most efficient working of the system.