Band 17, Heft 3 -

Analysis of losses in power distribution system and techniques to mitigate these are two active areas of research especially in energy scarce countries like Pakistan to increase the availability of power without installing new generation. Since total energy losses account for both technical losses (TL) as well as non-technical losses (NTLs). Utility companies in developing countries are incurring of major financial losses due to non-technical losses. NTLs lead to a series of additional losses, such as damage to the network (infrastructure and the reduction of network reliability) etc. The purpose of this paper is to perform an introductory investigation of non-technical losses in power distribution systems. Additionally, analysis of NTLs using consumer energy consumption data with the help of Linear Regression Analysis has been carried out. This data focuses on the Low Voltage (LV) distribution network, which includes: residential, commercial, agricultural and industrial consumers by using the monthly kWh interval data acquired over a period (one month) of time using smart meters. In this research different prevention techniques are also discussed to prevent illegal use of electricity in the distribution of electrical power system.

This paper presents the control of IPMSM drive in flux weakening region, for high speed applications. An adaptive hysteresis band current controller has been designed and implemented in this work to overcome the drawbacks which are present in case of conventional hysteresis band current controllers such as: high torque ripple, more current error, large variation in switching frequency etc. The proposed current controller is a hysteresis controller in which the hysteresis band is programmed as a function of variation of motor speed and load current. Any variation in those parameters causes an appropriate change in the band which in turns reduces the torque ripple as well as current error of the machine. The proposed scheme is modeled and tested in the MATLAB-Simulink environment for the effectiveness of the study. Further, the result is validated experimentally by using TMS320F2812 digital signal processor.

Heterogeneous energy prosumers are aggregated to form a smart grid based energy community managed by a central controller which could maximize their collective energy resource utilization. Using the central controller and distributed energy management systems, various mechanisms that harness the power profile of the energy community are developed for optimal, multi-objective energy management. The proposed mechanisms include resource-aware, multi-variable energy utility maximization objectives, namely: (1) maximizing the net green energy utilization, (2) maximizing the prosumers’ level of comfortable, high quality power usage, and (3) maximizing the economic dispatch of energy storage units that minimize the net energy cost of the energy community. Moreover, an optimal energy management solution that combines the three objectives has been implemented by developing novel techniques of optimally flexible (un)certainty projection and appliance based pricing decomposition in an IBM ILOG CPLEX studio. A real-world, per-minute data from an energy community consisting of forty prosumers in Amsterdam, Netherlands is used. Results show that each of the proposed mechanisms yields significant increases in the aggregate energy resource utilization and welfare of prosumers as compared to traditional peak-power reduction methods. Furthermore, the multi-objective, resource-aware utility maximization approach leads to an optimal energy equilibrium and provides a sustainable energy management solution as verified by the Lagrangian method. The proposed resource-aware mechanisms could directly benefit emerging energy communities in the world to attain their energy resource utilization targets.

The expected rise of plug-in electric vehicles (PEVs) will lead to a significant additional demand on low voltage distribution systems, and PEVs can challenge power quality and reliability of power systems if their charging is not coordinated. The PEV aggregator, acting as an intermedium between customers and the grid operator, can solve the charging management problem effectively. In this study, a decentralized scheduling scheme of aggregators for PEV charging based on Lagrange Relaxation method is proposed. At the same time, a centralized model of aggregators is also formulated in the same situation in order to make comparisons with the proposed scheme. The two charging models are both aimed at maximizing the charging profit of PEV aggregators considering constraints including users’ electricity demand, charging time, available capacity of distribution transformers, etc. A number of charging scenarios are simulated to evaluate the proposed method. The economic benefits, computational complexity and efficiency are compared and analyzed between the centralized charging and the decentralized charging. Simulation results verify that the decentralized charging strategy based on Lagrange Relaxation method gains charging profit close to the centralized charging yet is more efficient and realizable than the centralized charging.

Induction furnaces are used in wide quantity under different capacities for annual production of around 25 million tons of iron and steel in India. It plays a vital role in various manufacturing processes around the world for melting different types of metal scraps i. e. Copper, Cast Iron, Aluminium, Steel, Brass, Bronze, Silicon, Gold, Silver etc. which are further used in many other industrial applications. The induction furnace causes a huge disturbance to the utility and nearby consumers during its operation due to its non-linear characteristics. This is a serious phenomenon responsible for power quality degradation in the power system. This paper presents methodology to improve the power quality degradation caused by induction furnace using Dynamic Voltage Restorer (DVR) which is a type of custom power device. The real time data has been taken from an industry employing induction furnace for production of ingots from scrap material. The experimental readings are measured using power quality analyser equipment. The simulation of whole plant is done by analysing this same data and the simulation results are compared with actual onsite results. Then, solution methodology using DVR is presented which revealed that the implementation of DVR is an effective solution for voltage sag mitigation and harmonics improvement in induction furnace.

This paper proposes a stochastic optimization technique for solving the Voltage/VAr control problem including the load demand and Renewable Energy Resources (RERs) variation. The RERs often take along some inputs like stochastic behavior. One of the important challenges i. e., Voltage/VAr control is a prime source for handling power system complexity and reliability, hence it is the fundamental requirement for all the utility companies. There is a need for the robust and efficient Voltage/VAr optimization technique to meet the peak demand and reduction of system losses. The voltages beyond the limit may damage costly sub-station devices and equipments at consumer end as well. Especially, the RERs introduces more disturbances and some of the RERs are not even capable enough to meet the VAr demand. Therefore, there is a strong need for the Voltage/VAr control in RERs environment. This paper aims at the development of optimal scheme for Voltage/VAr control involving RERs. In this paper, Latin Hypercube Sampling (LHS) method is used to cover full range of variables by maximally satisfying the marginal distribution. Here, backward scenario reduction technique is used to reduce the number of scenarios effectively and maximally retain the fitting accuracy of samples. The developed optimization scheme is tested on IEEE 24 bus Reliability Test System (RTS) considering the load demand and RERs variation.

This paper proposes the integration scheme and operation of the Unified Power Quality conditioner (UPQC) with Photovoltaic source as distributed generations for power quality improvement. Thus, it provides a novel PV-grid integration configuration and prevents any adverse situation related to current or voltage in power system. Voltage related issues are maintained by the series part of UPQC and the current related issues are handles by shunt part of the UPQC. The various operation modes of PV-UPQC schemes are broadly classified according to the direction of power flow, (i) Interconnected mode, (ii) Islanding mode. PV-UPQC has advantage over the conventional UPQC scheme as it has developed the capability to compensate the voltage interruption problems Control algorithms for shunt and series part of the UPQC is implemented. Development of the proposed configuration has been designed in the laboratory with control algorithm implemented in dSPACE and results are discussed.

In this paper an improved over current relay coordination protection technique is proposed. The proposed technique eliminates the sympathetic/nuisance tripping in an interconnected distribution system. Nuisance trippings are eliminated by incorporating the additional selectivity constraints in conventional over current relay coordination problem. Whenever the fault is cleared only from one end of the line in bidirectional fault feed lines, transient network configuration comes into existence. Transient network configuration causes the redistribution of fault in the distribution system and leads to further nuisance tripping of relays. This problem in this paper is solved by minimising the operating time gap between primary relays located at near end and far end of a faulty line to the best minimum possible value. The differential search algorithm is applied for optimization of highly non-linear over current relay coordination problem in this paper. The result presented in this paper shows that the proposed over current relay coordination technique is immune against the sympathetic/nuisance tripping and operating time difference between primary relays at near end and far end is also minimised within acceptable time margin successfully.

This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wind farms is constructed, where includes three objective functions, operating cost, power loss, and pollutant emission. Combining the non-dominated sorting technique and the crowding distance index, the MOHLO method can be derived, which involves individual learning operator, social learning operator, random exploration learning operator and adaptive strategies. Both the proposed MOHLO method and non-dominated sorting genetic algorithm II (NSGAII) are tested on an improved IEEE 30-bus AC/DC hybrid system. Simulation results show that MOHLO method has excellent search efficiency and the powerful ability of searching optimal. Above all, MOHLO method can obtain more complete pareto front than that by NSGAII method. However, how to choose the optimal solution from pareto front depends mainly on the decision makers who stand from the economic point of view or from the energy saving and emission reduction point of view.

This paper presents a novel intelligent energy management system (IEMS) for a DC microgrid connected to the public utility (PU), photovoltaic (PV) and multi- battery bank (BB). The control objectives of the proposed IEMS system are: (i) to ensure the load sharing (according to the source capacity) among sources, (ii) to reduce the power loss (high efficient) in the system, and (iii) to enhance the system reliability and power quality. The proposed IEMS is novel because it follows the ideal characteristics of the battery (with some assumptions) for the power sharing and the selection of the closest source to minimize the power losses. The IEMS allows continuous and accurate monitoring with intelligent control of distribution system operations such as battery bank energy storage (BBES) system, PV system and customer utilization of electric power. The proposed IEMS gives the better operational performance for operating conditions in terms of load sharing, loss minimization, and reliability enhancement of the DC microgrid.

In this paper, a new heuristic algorithm is proposed for the optimum voltage control, which is applicable for the large Radial Distribution Systems (RDSs). In the RDSs, voltage levels at different buses can be maintained within the specified limits using the conductor grading or placing the Voltage Regulators (VRs) and capacitors at suitable locations. The proposed Back Tracking Algorithm (BTA) proposes the optimal location, number and tap positions of VRs to maintain the voltage profile within the desired limits and decreases losses in the system, which in turn maximizes the net savings in the operation of distribution system. In addition to BTA, an approach using the fuzzy logic called Fuzzy Expert System (FES) is also proposed, and the results of FES are compared with the results of BTA. This heuristic algorithm proposes the optimal location and tap setting of VRs, which contributes a smooth voltage profile along the network. It also used to access the minimum number of initially considered VRs, by moving them in such way as to control the network voltage at minimum possible cost. It is concluded that the FES also gives the optimal placement and the number along with the tap settings of VRs. The proposed FES contributes good voltage regulation, and decreases the power loss which in turn increases the net savings when compared to the BTA. The effectiveness of the proposed heuristic approaches are examined on practical 47 bus and 69 bus Radial Distribution Systems (RDSs).

This research paper discover the design of a shunt Passive Power Filter (PPF) in Hybrid Series Active Power Filter (HSAPF) that employs a novel analytic methodology which is superior than FFT analysis. This novel approach consists of the estimation, detection and classification of the signals. The proposed method is applied to estimate, detect and classify the power quality (PQ) disturbance such as harmonics. This proposed work deals with three methods: the harmonic detection through wavelet transform method, the harmonic estimation by Kalman Filter algorithm and harmonic classification by decision tree method. From different type of mother wavelets in wavelet transform method, the db8 is selected as suitable mother wavelet because of its potency on transient response and crouched oscillation at frequency domain. In harmonic compensation process, the detected harmonic is compensated through Hybrid Series Active Power Filter (HSAPF) based on Instantaneous Reactive Power Theory (IRPT). The efficacy of the proposed method is verified in MATLAB/SIMULINK domain and as well as with an experimental set up. The obtained results confirm the superiority of the proposed methodology than FFT analysis. This newly proposed PPF is used to make the conventional HSAPF more robust and stable.