Volume 24, Issue 5

In this paper, a dc–dc boost converter with continuous input is investigated. In addition to its input current, the converter offers the advantage of using a reduced number of electronic components. The objective is to design an appropriate controller for the nonlinear model taking into account the system nonlinearities, the uncertainty of the resistance load, the constrained fuel cell current, and the saturation of the control input signal (duty ratio).The point is that the converter presents a no minimum phase feature, and the duty ratio must constrain between 0 and 1. Then, a saturated nonlinear controller is elaborated. Detailed analysis and simulation show that the proposed controller meets all control objectives, namely: tight dc-bus voltage regulation and asymptotic stability of the closed-loop system. Experimental results are also given, which show the effectiveness of the controller.

The day-ahead and real-time congestion scheduling method for distribution network with multiple access to electric vehicle charging piles is studied to effectively solve the day-ahead and real-time congestion scheduling problem of distribution network. The charge adjustment strategy of charge and discharge service fee is established to realize the double response regulation between the distribution system’s scheduling organization and the charging pile operator; considering the adjustment strategy of charging service fee, a day-ahead congestion scheduling model is established with the goal of minimizing the charging cost of electric vehicles; based on the day-ahead congestion scheduling, a real-time congestion scheduling model is established to minimize the regional power fluctuation; the day-ahead and real-time congestion scheduling model is solved by differential evolution algorithm, and the optimal scheduling scheme is obtained. Experiments show that this method can reduce the line load rate of distribution network, avoid re-congestion, reduce the congestion scheduling cost and improve the security and economy of power grid operation.

This paper presents a novel real-time hybrid battery state of charge (SoC) and state of health (SoH) estimation technique with less computational effort for optimal operation in renewable energy integrated microgrid applications. The proposed SoC estimation technique utilizes battery terminal voltage and current information along with stress factors like battery charge–discharge rates and temperature effects to accurately estimate the SoC. In addition, it considers the open-circuit voltage (OCV) and SoC relation to dynamically recalibrate the SoC during idle conditions. The proposed SoH estimation technique uses a modified coulomb counting method and variation of battery capacity at different charge–discharge rates to precisely estimate the SoH of the battery. Simulation studies are carried out by considering the aging factor, temperature effect, and charge–discharge rates to analyze the performance of the proposed techniques under various dynamic conditions. A LabVIEW-based application is developed, and experimental verification in terms of estimation accuracy, real-time monitoring is carried out to verify the efficacy of the proposed technique. A comparative analysis with state-of-the-art estimation techniques is presented for validating the effectiveness and usefulness in real-time applications.

In this article, An Adaptive Single Carrier Modulation (ASCM) scheme based Multilevel Inverter (MLI) is proposed for the Transformerless Dynamic Voltage Compensator (ASCM-MLITDVC) to enhance the load Voltage Quality (VQ). The enhancement of VQ includes voltage sag, swell, unbalance and reducing Total Harmonic Distortion (THD). The Pulse Generation Unit (PGU) require more Carrier Waves (CW) for higher levels of MLI with respect to the number of switches used. This made the PGU complex, hence a carrier wave modulation scheme is proposed which can use it for n number of levels. The Position of CW is altered automatically with respect to the amplitude of Reference Staircase Waveform (RSW). The RSW is created by utilizing the Half Height Method and optimized NLM scheme which makes the least possible output THD is achieved. This scheme is implemented utilizing a real-time simulator OP4500. The results are obtained by conducting Power Hardware in Loop (P-HIL) test method to prove ASCM-MLITDVC can enhance load VQ with reduced THD.

This paper focuses on electric vehicles with dual motors combined with a planetary gear train. The combination of sun and ring motor allows for achieving higher efficiencies and satisfying power demands as compared to single motor powertrains. Primarily dual motor drive with planetary gear train is proposed and simulated with different drive cycles. Secondarily by extracting the motor efficiency maps working area of different modes, speed and efficiency are determined. Finally, an optimization algorithm was developed to automatically change speed and torque as per load demand. In this way, our work could represent a framework for improving efficiency performances and load demands of an electric vehicle.

Low-voltage distribution lines lack efficient monitoring. With the massive proliferation of distributed energy resource supply, it is necessary to establish an effective monitoring system for low-voltage distribution lines to ensure the electrical safety and continuity of power supply services. This paper proposes monitoring system for low-voltage distribution lines based on the Internet of things (IoT) technology. An IoT based monitoring system contains a large number of low-voltage monitoring terminals, different manufacturers, and different types of terminal equipment to solve the interconnection problem of information interaction. This paper first analyzes the terminal functional communication requirements based on IEC 61850 standards. Further, the information models required by the electrical volume collection and non-electric quantity collection functions are studied. The information modeling for different types of monitoring terminal equipment is presented and thus provide the configuration suggestions. The monitoring terminal based on the information model developed in this paper is applied in a university campus to realize the monitoring of low-voltage lines and distributed power supply.

Selective Harmonic Elimination PWM (SHEPWM) is a classical method of voltage control of the inverter while eliminating the undesirable lower order harmonics for low-frequency applications. Multivariable non-linear equations often result in SHE modulation scheme. In this work, a Most Valuable Player Algorithm (MVPA) has been applied to solve the non-linear SHE equations for the elimination of lower order harmonics in a cascaded H bridge multilevel inverter. The MVPA algorithm shows better convergence characteristics and wider modulation control compared to some popular meta-heuristic optimization methods. Optimized switching angles have been obtained for 5, 7 and 9 level inverters for different modulation index (MI) from 0.05 to 1. The Total Harmonic Distortion (THD) for the output voltage was found to be smoother compared to the powerful Differential Evolution (DE) algorithm for modulation index greater than 0.6. And for modulation index greater than 0.8, MVPA scores better than DE in terms of THD. Experimentally, the performance of MVPA has also been validated for 1-phase 5, 7 and 9 level cascaded H-bridge inverter and 5th, 5th and 7th, 5th, 7th, and 11th harmonic was eliminated respectively.

This paper proposes a new five-level double boost inverter based on switched capacitor technique. The proposed topology has fewer number of components and has the ability of balancing the voltage across the capacitor. The structure of the proposed topology is very simple and can be easily extended to higher number of voltage levels. The proposed generalised topology for higher gain is presented. This proposed 5-level inverter is compared with the state-of-art topologies to demonstrate the superior performance of the proposed topology. The proposed topology is simulated using MATLAB/Simulink by using the simplified control logic. Further, the power losses are calculated to find the efficiency of the proposed topology. Finally, this topology is tested in laboratory for the performance verification and the results are included.

The study investigates the implementation of novel Neuro-Fuzzy controllers to maintain the power quality for standalone Photovoltaic (PV)-Electrolyzer-Fuel Cell- Battery based power generation systems. Standalone solar based power generation systems are widely becoming popular particularly in the remote areas with no connectivity to a grid. There are challenges to maintain the power output to meet the demand in these standalone systems because of the random nature of solar irradiances, variation of load and no irradiance during night time. The battery bank is required to store the excess energy for use later when required. The battery bank is integrated into a standalone system through a bidirectional DC to DC converter. However, large batteries are costly and require maintenance. Hence, electrolyzer and fuel cell are also integrated with respective DC to DC converters to make a cost effective operation. Small size battery bank is used to stabilize dc-link voltage during transients due to slow dynamics of electrolyzer and fuel cells. The maximum power point tracking (MPPT) device and perturbed and observed algorithm is used for the PV system to operate at maximum utilization. In this paper Neuro-Fuzzy controller based novel controllers are implemented to inverter and DC to DC converters for supplying quality power to both three phase and single phase loads at AC load bus. The presented results are examined through hardware-in-loop on the platform of OPAL-RT to investigate the proposed controllers in all the possible scenarios of a 1 MW standalone system. Results show that the proposed controllers improve the power quality and eliminate the frequency oscillations from the system voltage.

Secondary system is an important link that affects the reliable operation of power system. However, the current improvement measures for accurate data acquisition and reliable operation in secondary systems are mainly concentrated at the equipment level. The solution at the equipment level not only increases the complexity of the system, but also can only optimize a single link or problem, which is difficult to improve the overall system level. In order to enhance the information accuracy, operation and maintenance precision and operation reliability of smart substation secondary system, this paper proposes bad data identification and fault diagnosis methods based on secondary system information redundancy. Firstly, according to the analysis of secondary information redundancy, this paper constructs the data information redundancy model of the smart substation secondary system. Then the data information state estimation method based on the least square method and the bad data identification method based on the information redundancy are proposed. Finally, case analysis is carried out to verify that the proposed method can effectively increase the information accuracy of smart substation, which also provides new research route and foundations for secondary system fault diagnosis.

In the process of operation and maintenance of digital three-dimensional substation, affected by the fault feature extraction method, the anti-interference ability of the detection method is poor. Therefore, a digital three-dimensional substation fault detection method based on singular value decomposition is proposed. According to the digital twin five-dimensional concept, a three-dimensional management and control model of the substation is built, the real-time collected current signal is denoised, and then a new feature extraction method is designed by using the singular value decomposition theory, and the characteristic parameters of the fault signal are defined. Finally, the maximum wavelet singular value is calculated by combining the wavelet transformation method and the singular value decomposition principle. The fault detection is completed according to the mutation of singular value. The simulation experiment results show that the proposed fault detection method has improved the anti-interference ability by 44 and 86%. It is verified that the method proposed in this study has good fault detection effect, and provides auxiliary decision-making basis for relevant departments.

With the proposal of the concept of ubiquitous power Internet of Things and the further promotion of blockchain strategy, modern information technologies such as the Internet of Things and cloud computing have been widely used in the power system. While the power grid is becoming more intelligent, it is also accompanied by the emergence of various security threats, and the power grid will face greater challenges and pressures. In this paper, aiming at the current situation of information security faced by the power grid blockchain under the cloud Internet of Things, a multi-authorization center ciphertext policy attribute encryption access control method is constructed for the power grid blockchain. It adds the function of visitor attribute update revocation, aggregates multi-dimensional data into ciphertext, and uses CP-ABE encryption to implement fine-grained access control at the dimension level. Through experimental analysis, in terms of security and functionality, this method can resist physical collision attacks and manage access rights flexibly. In terms of encryption and decryption efficiency, the encryption and decryption calculation speed of the proposed method is 6.2–15.6% higher than that of the traditional method, and the time consumption efficiency is more than 5% higher than that of other methods. It proves that this model has more advantages for open network environments such as power grid blockchain with high confidentiality requirements and large scale of users. It is of great significance to study the data security and privacy protection methods of the power grid blockchain.