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The quality and reliability of the electrical components and electronic devices are well known as very sensitive measurements. The magnitude variations are much greater than in the static electrical characteristics. The excess noise is caused by faults and the system was not ideal. While the excess noise is an overall quality measure, there may be multiple physical processes. These noise effects are additive, so the noise is not as important as a diagnostic tool.
There is still no detailed understanding of certain sources of noise, such as those present in some semiconductor devices.
Recently, the size of devices has continued to increase to make the noise signal more significant than the actual signal and the number of defects in the signal has decreased. This has led to a growing trend to research the signal varies from the time spectral to the noise level. The innovations in the subject are reviewed. This paper shows how to resolve problems of electric noise quality and to discuss various mitigation techniques to improve the problem, including the power quality enhancement devices described in recent years.
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The need for electricity increases day by day and the quality of electricity generated in this industry becomes the key issue. Therefore, power quality should be efficiently preserved.
An unwanted electrical signal with a frequency usually lower than 200 kHz is correlated with electrical noise. Noise disturbances are different from harmonic distortions and transients since they do not behave like waveforms. Noise sources include electronic power supplies, control circuits, arcing equipment, and power switches.
It has been noted that the consequences of noise will be worsened when grounding is introduced within the power distribution system. An example of an electrical noise.
The electrical noise is a high-frequency interference within the 7000 Hz to over 50 MHz frequency range. Noise can be emitted and picked up by a power cord serving as an antenna, or it can be emitted via a power line. These disturbances may be caused by radio frequency interference, such as radio, TV, cellular and microwave transmission, radar, arc welding, and distant lightning. Noise can also be caused by electromagnetic interference generated by heaters, air conditioners, white goods, and other thermostat-controlled or motor-operated devices.
Although generally non-destructive, electrical noise can sometimes pass through the power supply as if it were a signal and erase stored data or cause incorrect data output. Problems occur when intermittent, high-frequency voltages commonly called 'line noise' disrupt microelectronic circuitry, which can be divided into one of two categories: differential (normal) mode or common mode.
In differential (normal) mode noise, the source of noise occurs through power supply lines and is in series with the power supply line, and the current of noise flows in the same direction as the current supply. It is known as 'differential mode' because of the opposite direction of outgoing and returning current.
Common noise mode is noise in which a stream of noise that passes through the ground or returns to the supply line through stray capacitance. It is called 'common mode' noise because on the positive (+) and the negative (-) sides of the power supply, the direction of the noise currents is the same. The power supply lines do not display a noise voltage. As described above, emissions are performed for these forms of noise. Noise currents flow in the power supply lines and so noise is radiated.
Many types of power improvement devices were built to improve the quality of power as well as to protect the equipment [6]. The following power tools provide optimal quality of power. First one is Adaptive Digital Filters, Window Based FIR Filter, Ground System, and Conventional Filter.
First we use an adaptive predictor filter combined with an adaptive digital filtering to do the noise reduction process in this article. The benefits of the adaptive filter are that it can dynamically handle the coefficients, based on input signals. Figure 3 demonstrates how the adaptive predictor filter applied to this analysis is organized.
Formula for LMS Algorithm:
μ(n)=Step size parameter for optimal coefficient adjustment
They can break the adaptive filter into two processes. The first is the filtering method called the linear error predictor filter which includes the finite impulse response filter (FIR). Signal processing technique, subtracting the predicted continuous signal from the input signal can reduce the continuous noises. This forecasting method operating in the time domain allows use of the noise / disturbance statistical characteristics. The second involves an adaptive method using an adaptive algorithm to measure the filter coefficients h such that for each sampling period it is gradually modified to optimal values depending on the input signals.
This approach has a function that needs neither a priori Partial Discharge (PD) signal information, nor noise characteristics. The solid and dotted lines indicate the movement of the filtering equation and the adaptive method, respectively. This used the Least Mean Square (LMS) algorithm as the adaptive algorithm, which uses a step size parameter to search for h(n) after repeated test and error:, μ, instead of direct calculation, from the point of view of the Calculation time speed-up. Notwithstanding, it is important to determine the value of, μ, so that the author adopted the normalized LMS algorithm. The equation of , μ (n) is shown in the following.
Digital filters were divide into two forms including FIR and IIR i.e. respectively Finite Impulse Response and Infinite Impulse Response. This classification is made based on the system's type of unit-impulse response. FIR filter has finite impulse response time. FIR filter feedback is based on present and past inputs only as FIR filters are completed using framework without feedback. Through this process, the optimal frequency response is evaluated as Hd(ω) and the corresponding unit sample response as hd(m) is calculated using inverse Fourier transform. The derivative of Hd(ω) and hd(n) is as follows.
Formula for Impulse Response Truncation:
hd(m)×Window Function=Truncated Impulse Response
Direct truncation of terms H(m) of the Fourier series hd(m) to M Due to the non-uniform convergence at discontinuity of the Fourier series, which perform ripples in the characteristic frequency response H(ω).It gives Gibbs phenomenon means in the vicinity of the filter's band edge shows oscillatory behaviour. Consequently, the frequency feedback we obtained using Equation 7 Does the frequency domain contain ripples.
There are many noise problems connected with system grounds. There clearly does not exist the traditional idea of a 'common system ground' that is, a conductor on which all points are at a similar stable potential. A load ZL was obtained by a square-wave generator S, with one side of the load and generator grounded by a common conductor at point G. In this idealized direction all lines are examined as pure conductors, and it is assumed that any point on the grounded conductor serves equally well as a common reference.
Yet actual circuits do not behave like the idealized circuit of Figure 4 and Figure 5 in its more practical form obtained the same simple circuit with all the extraneous properties inherent in conductors. These include inductance, R skin skin-effect resistance, C-capacity, T-transformer coupling, and R dc-resistance. Despite it is well known that these extraneous properties exist, their outcomes are generally assumed to be negligible. This is far from the case. Some startling answers tumble out when typical values are enforced into standard formulas. The successive examples have been determined using regular Terman formulas .
Multichannel noise reduction in the frequency domain is behaved in the conventional filter by applying a complex-valued linear filter, h(f), of length M, to the observation signal vector, y(f).
Formula for Conventional Filtering:
X^(f)=H(f)⋅Y(f)
where the filter output, 𝑋̂(𝑓), is an estimate of X1(f), 𝑋fd(𝑓) = 𝑋1(𝑓)𝐡𝐻 (𝑓)𝐝(𝑓) is the filter covet signal, and 𝑉rn(𝑓) = 𝐡𝐻 (𝑓)𝐯(𝑓) is the residual noise. The two terms on the right-hand side of (1) are uncorrelated.
Hence, the variance of 𝑋̂(𝑓) is also the sum of two variances
where 𝜙𝑋fd (𝑓) = 𝜙𝑋1 (𝑓)| |𝐡𝐻 (𝑓)𝐝(𝑓)| 2 is the variance of the filtered covet signal and 𝜙𝑉rn (𝑓) = 𝐡𝐻 (𝑓)𝚽𝐯(𝑓)𝐡(𝑓) is the variance of the residual noise. From (2), we deduce that the narrowband output SNR.
In conclusion, these problems in terms of power quality are undesirable phenomena that can be avoided using all techniques but are not limited to the techniques discussed. Every application does not have one mitigation technique and the power supplies are aimed at providing better power quality. It means that the issue of Power Quality cannot be overcome but it can be reduced and avoided. The best way to avoid the problem of power quality is to ensure that the power supply system compatibles with all equipment to be built on the manufacturing plants. This can be accomplished by acquiring equipment with sufficient technical specifications that integrate power quality in its electric working environment.
Mitigating Electrical Noise: Techniques and Innovations. (2024, Feb 18). Retrieved from https://studymoose.com/document/mitigating-electrical-noise-techniques-and-innovations
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