No abstract

With the rising load demand and power losses, the equipment in the utility network often operates close to its marginal limits, creating a dire need for the installation of new Distributed Generators (DGs). Their proper placement is one of the prerequisites for fully achieving the benefits; otherwise, this may result in the worsening of their performance. This could even lead to further deterioration if an effective Energy Management System (EMS) is not installed. Firstly, addressing these issues, this research exploits a Genetic Algorithm (GA) for the proper placement of new DGs in a distribution system. This approach is based on the system losses, voltage profiles, and phase angle jump variations. Secondly, the energy management models are designed using a fuzzy inference system. The models are then analyzed under heavy loading and fault conditions. This research is conducted on a six bus radial test system in a simulated environment together with a real-time Power Hardware-In-the-Loop (PHIL) setup. It is concluded that the optimal placement of a 3.33 MVA synchronous DG is near the load center, and the robustness of the proposed EMS is proven by mitigating the distinct contingencies within the approximately 2.5 cycles of the operating period.

Distribution networks are inherently radial and passive owing to the ease of operation and unidirectional power flow. Proper installation of Distributed Generators, on the one hand, makes the utility network active and mitigates certain power quality issues e.g., voltage dips, frequency deviations, losses, etc., but on the other hand, it disturbs the optimal coordination among existing protection devices e.g., over-current relays. In order to maintain the desired selectivity level, such that the primary and backup relays are synchronized against different contingencies, it necessitates design of intelligent and promising protection schemes to distinguish between the upstream and downstream power flows. This research proposes exploiting phase angle jump, an overlooked voltage sag parameter, to add directional element to digital over-current relays with inverse time characteristics. The decision on the direction of current is made on the basis of polarity of phase angle jump together with the impedance angle of the system. The proposed scheme at first is evaluated on a test system in a simulated environment under symmetrical and unsymmetrical faults and, secondly, as a proof of the concept, it is verified in real-time on a laboratory setup using a Power Hardware-in-loop (PHIL) system. Moreover, a comparative analysis is made with other state-of-the-art techniques to evaluate the performance and robustness of the proposed approach.

Integration of renewable energy resources and conventional grids leads to an increase in power quality issues. These power quality issues require different standards to be followed for accurate measurement and monitoring of various parameters of the power system. Conventional power quality analyzers (PQAs) are programmed to a particular standard and cannot be reconfigured by the end user. Therefore, conventional PQAs cannot meet the challenges of a rapidly changing grid. In this regard, a Compact RIO-based (CRIO-based) PQA was proposed, that can be easily reprogrammed and cope with the challenges faced by conventional PQAs. The salient features of the proposed PQA are a high processing speed, interactive interface, and high-quality data-storage capacity. Moreover, unlike conventional PQAs, the proposed PQA can be monitored remotely via the internet. In this research, a hardware-in-loop (HIL) simulation is used for performing the power-quality assessment in a systematic manner. Power quality indices such as apparent power, power factor, harmonics, frequency disturbance, inrush current, voltage sag and voltage swell are considered for validating the performance of the proposed PQA against the Fluke’s PQA 43-B.

Solar energy is becoming the mainstream energy source by drawing considerable attention of analysts these days. The output power of a photovoltaic (PV) system uctuates with temperature and sunlight, a ecting its e ciency. To extract accessible power by a PV system, Maximum Power Point Tracking (MPPT) method is adopted. A famous strategy regularly utilized due to its simplicity and low cost is the Perturb and Observe (PO) algorithm. However, there are a few downsides with PO algorithm, which result in power loss and low e ciency. We compared the performance of the conventional PO with some enhancements, especially a recent PO variant, for MPPT. Experiments were conducted at di erent irradiances and temperature levels in two ways, namely with load and with battery, by conventional PO and its variants. The strategy to reach optima and stability of the methods were discussed. The PO variants were rated from the viewpoints of stability, accuracy, post-MPP oscillations, and tracking speed. The modi cations were proven to be fruitful for the practitioners working with MPPT in PV solar systems using PO algorithms. Simulation results were validated using real-time experimental results. The new PO variant appeared to be a reliable computing algorithm for MPPT in solar PV systems.