In modern power systems, condition based monitoring and diagnosis is essential to ensure the effective and reliable operation of different high voltage equipment (HVE). Compared to other monitoring techniques, partial discharges (PD) measurement is considered as a key method for assessing the insulation health condition. The benefits of PD condition monitoring of HVE can be extended by proper detection, identification, and interpretation of PD signal. Among both on-line and off-line PD monitoring techniques, on-line PD monitoring is a very promising technique that assists in robust monitoring system which reduces the power failure incidents in power system components. Therefore, to understand recent developments and trends in theory and in practice, it is necessary to establish a holistic analysis of current on-line PD monitoring techniques for HVE in power systems. This paper presents an intensive literature review of current on-line PD monitoring techniques used for different high voltage electric components in power system. Finally, a smart PD monitoring techniques based on wireless sensor board is proposed. The proposed smart PD monitoring framework may be used to correctly estimate the insulation degradation in HVE and enhance the overall performance of power systems.

Load scheduling and peak demand shaving are two critical aspects of the utility grid operation that help both the grid operators as well as end-users. This paper proposes a two-stage community home energy management system for microgrids. The first stage deals with the dynamic clustered community load scheduling scheme. Comparatively flatter power demand was attained using particle swarm optimization (PSO) incorporating user-defined constraints. The new arising or remaining peaks as a consequence of consumer constraints are catered to in the second stage. The second stage proposes a rule-based peak shaving management method for the photovoltaic (PV) systems that are connected to the grid and the battery energy storage systems. The proposed technique determines the dynamic demand and feed-in limits based on the estimations of the upcoming day’s load demand and PV power profiles. Also, the study presents an optimal rule-based management technique for peak shaving of utility grid power that sets the charge/discharge day ahead schedules of the battery. For peak energy minimization, PSO is used to calculate the optimal inputs needed for implementing the appropriate rule-based management strategy. MATLAB is used to test the proposed method for different PV power and load demand patterns, thus, achieving an average improvement of 8.5%.

This paper presents two battery energy management (BEM) techniques for an electric vehicle (EV) traction system which incorporates an indirect field-oriented (IFO) induction motor (IM) drive system. The main objective of the proposed BEM techniques is to regulate the IM’s speed while minimizing the lithium-ion (Li-ion) battery bank state of charge (SOC) reduction and state of health (SOH) degradation. In contrast to most of the existing work, the proposed BEM techniques operate without any prior knowledge of driving profiles or road information. The first BEM technique incorporates two cascaded fuzzy logic controllers (CSFLC). In CSFLC, the first fuzzy logic controller (FLC) generates the reference current signal for regulating the motor speed, while the second FLC generates a variable gain that limits the current signal variation based on the battery SOC. The second BEM technique is based on model predictive control (MPC) which generates the current signal for the speed regulation. However, this work introduces a new way of tuning the MPC input weight using battery information. It features a fuzzy tuned model predictive controller (FMPC), where an FLC adjusts the input weight in the MPC objective function such that the battery SOC is considered while generating the command current signal. Furthermore, this work utilizes a model-in-loop strategy comprising a Chen and Mora (CM) battery model and the experimentally obtained battery bank power consumption to estimate the increase in battery bank runtime and lifetime. A real-time implementation is carried out on a prototype EV traction system using the New European Drive Cycle (NEDC) and the Supplemental Federal Test Procedure (US06) drive cycles. The experimental results validate that the proposed CSFLC and FMPC BEM techniques exhibit a lower reduction in the battery SOC and SOH degradation, thus prolonging the battery bank runtime and lifetime as compared to the conventional FLC and MPC speed regulators. Further experimentation demonstrates the superiority of the FMPC technique over the CSFLC technique due to the lesser computational burden and higher average energy saving.

In modern distribution networks, integration of distributed generations (DGs) at consumer premises is common. However, islanding detection in such a grid‐tied distributed generation (GTDG) network is the topmost challenge to ensure power quality and reliable operation. This paper presents a modified passive islanding detection scheme (MPIDS) for the GTDG network, using a One‐D recursive Median filter (ODRMF) algorithm. Firstly, at the point of common coupling (PCC), 3‐phase voltage is acquired. Next, the ODRMF is employed to extract 3rd harmonic and residuals from the acquired voltage signal. Afterward, two novel islanding detection indices developed are: (1) First change detection (FCD), which is calculated from the residuals, and (2) delta selected harmonic distortion (∆SHD), which is computed from 3rd harmonic content of the voltage signal. Finally, variations in the two indices are compared with the threshold to identify islanding. To validate the efficacy of the presented MPIDS, rigorous simulations in MATLAB/ Simulink are conducted on the UL‐1741 and IEEE 13‐bus GTDGs systems under different conditions. The results illustrate that the presented strategy is highly reliable in all assessed conditions and can detect the islanding as well as non‐islanding incidents effectively under balanced and unbalanced load/generation conditions with a very low non‐detection zone.

Microgrids are the future version of advanced distribution networks due to the fast growth of renewable energy resources near consumers’ side. The microgrids are operated in on-grid mode (OGM) with the utility grid, and isolation mode (IM) without the utility grid. This dual operational mode causes protection and control challenges in the microgrids. This research paper suggests an advanced hardware-supported fault detection, phase identification & localization method for AC microgrids. The scheme deploys a Discrete Kalman Filter (DKF) for state estimation of voltage and current signals. Then, a Mathematical Morphology (MM) is engaged for generating a novel fault detection/classification index named segregated energy signature (SES) from estimated voltage and current signals. The system is considered to be faulty if the SES is higher than a predefined threshold setting, while phase identification is achieved by default because of the per-phase implementation of DKF&MM. Moreover, the directional features of the cumulative energy signature (CES) are also computed from MM-based non-fundamental current and voltage to localize the faulty section. The established scheme is tested on the CIGRE microgrid benchmark test bed on Matlab-Simulink software. In addition, the suggested method is also examined on the dSPACE MicroLab testing hardware setup in the Smart grid lab at USPCAS-E, NUST, Pakistan. The result illustrates that the proposed scheme successfully detects, classifies, and localizes the low impedance fault (LIF) as well as high impedance fault (HIF) in both operational modes and topological structures with 96.6% accuracy.