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Abstract This article presents the design and optimisation of a sub‐1 GHz class‐F ultra‐low power (ULP) power amplifier (PA) in 65 nm Complementary Metal Oxide Semiconductor (CMOS) technology. An envelope tracking (ET) supply biasing technique is adopted to improve the efficiency of class‐F PA. The ET consist of a pre‐amp right before the detector in order to enhance the efficiency and save adequate amount of dc power consumption. The PA consists of two cascode cells terminated as class‐F with gate‐to‐drain feedback in order to enhance linearity and limit any harmonic component from the input signal. The novel design consumes a dc power of 3.75 mW, power added efficiency of 37.1%, operating at 915–925 MHz unlicensed band and total saturated output power of 22 dBm including 14 dBm power gain at PA, which qualifies under long‐range low power wireless local area network IEEE 802.11ah standard. The inductor‐less design for ET supply bias reduces the chip layout size to 0.13 mm2 only.

This study investigates the impact of Particle Swarm Optimization (PSO) on power loss and production cost reduction in overloaded transmission lines. PSO is used to manage power flow in congested transmission lines and reduce power losses. The proposed technique was tested using the IEEE large-scale 57-bus test system. Optimal power flow (OPF) is crucial for modern power systems, aiming to minimize active and reactive power distribution while considering technological and economic constraints. PSO is presented as a solution to the OPF problem, focusing on actual power losses, hypothetical power losses, and overload consequence. PSO is presents.as a solution, and various methods, such as genetic algorithms and linear programming, will be used to compare PSO results. The suggested PSO yields reduced power losses with higher loads in case studies. The approach is evaluated for IEEE -57 buses and produces superior results when used with the MATLAB environment. PSO reduces the target function's real power loss from its starting condition (24.98) MW to its ideal state (16.75) MW with a reduction of 8.23 MW. Additionally, PSO reduces the target function's active power loss by 20% overload from its initial state (56.042 MW) to the optimal state of 46.23 MW with a reduction of 9.812 MW.

In the era of expansion of smart sensing interconnected devices and their growing application in complex systems, the application of wireless communication technology becomes evident. Many wireless technologies are developed to facilitate the growth of systems such as the Internet of Things and Smart Cities. The application of a particular wireless technology in a particular system depends on many factors, such as purpose, requirements, complexity, range, and node deployment. IEEE 802.15.4 is a technical standard that defines the operation of low-rate wireless personal area networks. It is used as the basis for a group of network standards and protocols designed for wireless sensor networks. In this article, the basic features of the emerging IEEE 802.15.4g SUN low-powered wireless network standard, its application scenarios, and performance analyses are presented.

In this paper, the design and implementation of a novel recursive method for the power measurement ac-cording to the IEEE Standard 1459-2010 have been described. The most important parts are adaptive band and low-pass FIR filters that extract fundamental and dc components, respectively. In addition, by using oversampling techniques and decimation filters, coefficient sensitivity problems of the large-order FIR comb cascade structure are overridden and the parameter estimation accuracy is improved. The symmetrical components are estimated through a transformation matrix of adaptive phase shifters. The effectiveness of the proposed techniques is demonstrated by simulation results.

This paper proposes an accurate and computationally efficient implementation of the IEEE Std. 1459-2010 for power measurements. An implementation is based on digital resonators embedded in a feedback loop. In the first algorithm stage, the unknown signal harmonic parameters are estimated. By this, the voltage and current signals are processed independently of each other. In the second algorithm stage, the unknown power components are estimated (calculated) based on estimated spectra. To demonstrate the performance of the developed algorithm, computer-simulated data and laboratory testing records are processed. Simple LabView implementation, based on the point-by-point processing feature, demonstrates techniques modest computation requirements and confirms that the proposed algorithm is suitable for real-time applications.

The introduction of the cyber-physical system (CPS) into power systems has created a variety of communication requirements and functions that existing legacy systems do not support. To this end, the IEEE 1815.1 standard defines the mapping between existing distributed network protocol networks and IEC 61850 networks that reflect new requirements. However, advanced CPS cyberattacks have been reported, and in order to address cyberattacks, security research on new power systems that use network devices and heterogeneous communication is necessary. In this study, we propose an intrusion detection system for an IEEE 1815.1-based power system using CPS. We 1) analyze an IEEE 1815.1-based power system network and propose a suitable application method for an intrusion detection system, 2) suggest a bidirectional recurrent neural network-based anomaly detection system for an IEEE 1815.1-based network, and 3) demonstrate the verification of the proposed technique using various power system-specific attack data, including real power system using CPS network traffic, CPS malware behavior (CMB), false data injection (FDI), and disabling reassembly (DR) attacks. Proposed technique successfully detected five types of CMB attacks, three types of FDI and DR attacks.

Nowadays, electrical energy needs are increasing rapidly as a result of technological developments. In order to meet this growing demand, power plants have been built. In this study, voltage stability in IEEE 14-bus power system was investigated by means of Extreme Learning Machine (ELM). For this purpose, the IEEE 14-bus power system model was built in Matlab environment and load flow analysis for this model was performed by using the Newton-Raphson Method (NRM). In this power system, the voltage stability was evaluated by calculating the Line Stability Index (LSI). In the load flow analysis, the active and reactive powers of all bus were increased by 0.05 step (pu) and a total of 1000 active power, reactive power, voltage, and phase angle of the respective bus were obtained for each busbar. These values were used to calculate the values of LSI. The inputs of ELM are selected as active power, reactive power, voltage and phase angle of the respective bus. The output of the NRM was determined as LSI values. The test performance of the NRM is given by 5-fold cross-validation. In addition, the ELM’s performance was investigated for the different number of hidden layer cell numbers and different types of activation functions. The proposed method provides the best test performance in case of ELM with a hidden layer cell number of 100 and the tangent sigmoid activation function. From the obtained results, it is seen that ELM predicted LSI with a very high performance in determination of voltage stability in IEEE 14-bus power systems.

An energy efficient, low-power 10-bit asynchronous successive approximation register (SAR) analog-to-digital (ADC) converter with the sampling frequency of 8 MS/s is presented for IEEE 802.15.1 IoT sensor based applications. An improved common mode charge redistribution algorithm is proposed for binary weighted SAR ADC. The proposed method uses available common mode voltage (VCM) level for SAR ADC conversion, and this method reduces the switching power by more than 12% without any additional DAC driver as compared to merged capacitor switching (MCS). Mathematical analysis of the proposed switching scheme results in the lower or equal power consumption for every digital code as compared to MCS. A two stage dynamic latched comparator with adaptive power control (APC) technique is used to optimize the overall efficiency. Furthermore, to minimize the digital part power consumption, a modified asynchronous SAR logic with digitally controlled delay cells is proposed. High efficiency with low power consumption makes it suitable for low power devices especially for IEEE 802.15.1 IoT sensor based applications. The proposed prototype is implemented using 1P6M 55 nm complementary metal-oxide-semiconductor (CMOS) technology. The measurement results that the proposed circuit achieves are 9.3 effective number of bits (ENOB) with signal-to-noise and distortion ratio (SNDR) of 58.05 dB at a sampling rate of 8 MS/s. The power consumption of SAR ADC is 45 μW when operated at 1 V power supply.

The previous research on the controllability of complex networks is qualitative issue which can only identify whether the network is controllable or not, and it is difficult to assess the controlled ability of network. In this paper, a quantitative measurement index of network controllability is introduced. And the index is used to study the controllability of the IEEE power network system. The relationship between controllability and network parameters (including the average degree, the clustering coefficient, the average path length) of power network is analyzed by comparing with the ER random network, WS small world network, BA scale-free network and CM network. The results show that the power networks have a small average path length and a higher clustering coefficient, which belong to the small world network. And, the controllability of power networks is larger than the corresponding model networks, indicating that power networks are relatively easier to be controlled.

The participation of power users in grid-side interactive power and auxiliary services has attracted increasing attention. The analysis of users’ interaction power behaviors is a core task. By combining the self-organizing map (SOM) neural network and the K-means clustering algorithm, a self-organizing-center K-means algorithm is developed in this paper for conducting a cluster analysis of users’ interaction electricity consumption behaviors. This approach can realize more accurate recognition and fast clustering. First, the principle of K-means algorithm in the self-organizing center is analyzed, and its advantages in the clustering analysis of electricity consumption over the traditional clustering algorithm are demonstrated. Then, in the context of peak-to-valley time-of-use electricity pricing, the adjustment potential index, which is based on user psychology, is developed, and a cluster analysis of users’ electricity consumption behaviors that is based on load data and the adjustment potential index is conducted. Finally, the daily load data of users within the jurisdiction of a power company are studied, and the two-stage clustering results that are obtained via the self-organizing-center K-means algorithm are compared with the clustering results that are obtained via the K-means algorithm, which demonstrates the advantages of the self-organizing-center K-means algorithm that is based on the adjustment potential index in the accurate recognition and accurate clustering of users.

This paper proposes a deterministic algorithm to scale the reference currents of a shunt active power compensator (SAPC) based on IEEE Std. 1459 power decomposition when SAPC maximum output compensating current is going to be exceeded. The selective SAPC is proposed to improve power quality and energy efficiency in power networks by means of the cancelation or reduction of the non-efficient powers (Q1+, SU1, SeN). The non-efficient powers can be reduced in six possible sequences according to the priority of compensation. When SAPC maximum output current capacity is exceeded, the proposed algorithm limits the SAPC output compensating currents and the non-efficient currents can only be partially reduced in the power network. The reduction of the non-efficient powers depends on the selected compensation sequence. Experimental results for several compensation sequences demonstrate the appropriate operation of the selective SAPC using the proposed scaling algorithm.